Amir H. Gandomi is a Professor of Data Science at the Faculty of Engineering & Information Technology, University of Technology Sydney. Prior to joining UTS, Prof. Gandomi was an Assistant Professor at the School of Business, Stevens Institute of Technology, USA and a distinguished research fellow in BEACON center, Michigan State University, USA. Prof. Gandomi has published over one hundred and eighty journal papers and seven books which collectively have been cited more than 15,000 times (H-index = 58). He has been named as one of the most influential scientific mind and Highly Cited Researcher (top 1%) for three consecutive years, 2017 to 2019. He also ranked 18th in GP bibliography among more than 12,000 researchers. He has served as associate editor, editor and guest editor in several prestigious journals such as AE of SWEVO, IEEE TBD, and IEEE IoTJ. Prof Gandomi is active in delivering keynotes and invited talks. His research interests are global optimisation and (big) data analytics using machine learning and evolutionary computations in particular.
Can supervise: YES
- Data Analytics
- Evolutionary Computation
- Genetic Programming
- Operation Research
- Artificial Intelligence
- Smart Cities and Systems
- Data Driven IoT
- Evolutionary Computation
- Multivariate Data Analysis
© Springer International Publishing Switzerland 2015. This contributed volume, written by leading international researchers, reviews the latest developments of genetic programming (GP) and its key applications in solving current real world problems, such as energy conversion and management, financial analysis, engineering modeling and design, and software engineering, to name a few. Inspired by natural evolution, the use of GP has expanded significantly in the last decade in almost every area of science and engineering. Exploring applications in a variety of fields, the information in this volume can help optimize computer programs throughout the sciences. Taking a hands-on approach, this book provides an invaluable reference to practitioners, providing the necessary details required for a successful application of GP and its branches to challenging problems ranging from drought prediction to trading volatility. It also demonstrates the evolution of GP through major developments in GP studies and applications. It is suitable for advanced students who wish to use relevant book chapters as a basis to pursue further research in these areas, as well as experienced practitioners looking to apply GP to new areas. The book also offers valuable supplementary material for design courses and computation in engineering.
Analysis of Turbulent Flows is written by one of the most prolific authors in the field of CFD. Professor Tuncer Cebeci calls on both his academic and industrial experience from teaching aerodynamics at SUPAERO, and directing DMAE at ONERA when presenting this work. Each chapter has been specifically constructed to provide a comprehensive overview of turbulent flow and its measurement. Analysis of Turbulent Flows serves as an advanced textbook for PhD candidates working in the field of CFD, making this book essential reading for researchers, practitioners in industry and MSc and MEng students. Key features include; An overview of the development and application of Computational Fluid Dynamics (CFD), with real applications to industry A free-to-access companion website which contains computer programs suitable for solving non-linear equations that arise in modeling turbulent flows Contains a unique section on 'Short-cut' methods - simple approaches to practical engineering problems. About the author; Professor Tuncer Cebeci Chair of the Department of Aerospace Engineering, California State University, Professor Cebeci is widely regarded as an expert in the field of Turbulent Flows and has received many accolades for his work. He was named the first Distinguished Professor in the California State University System, and he received numerous awards including Fellow of the American Institute of Aeronautics and Astronautics. He also received the Presidential Science Award from Turkey. An overview of the development and application of Computational Fluid Dynamics (CFD), with real applications to industry. A free-to-access companion website which contains computer programs suitable for solving non-linear equations that arise in modeling turbulent flows. Contains a unique section on 'Short-cut' methods - simple approaches to practical engineering problems. © 2013 Elsevier Inc. All rights reserved.
Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions. It can help new researchers to carry out timely research and inspire readers to develop new algorithms. With its impressive breadth and depth, this book will be useful for advanced undergraduate students, PhD students and lecturers in computer science, engineering and science as well as researchers and engineers. Focuses on the introduction and analysis of key algorithms Includes case studies for real-world applications Contains a balance of theory and applications, so readers who are interested in either algorithm or applications will all benefit from this timely book. © 2013 Elsevier Inc. All rights reserved.
Ashrafian, A, Gandomi, AH, Rezaie-Balf, M & Emadi, M 2020, 'An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement', MEASUREMENT, vol. 152.View/Download from: Publisher's site
Aziminezhad, M, Mardi, S, Hajikarimi, P, Nejad, FM & Gandomi, AH 2020, 'Loading rate effect on fracture behavior of fiber reinforced high strength concrete using a semi-circular bending test', CONSTRUCTION AND BUILDING MATERIALS, vol. 240.View/Download from: Publisher's site
Behera, TM, Mohapatra, SK, Samal, UC, Khan, MS, Daneshmand, M & Gandomi, AH 2020, 'I-SEP: An Improved Routing Protocol for Heterogeneous WSN for IoT-Based Environmental Monitoring', IEEE INTERNET OF THINGS JOURNAL, vol. 7, no. 1, pp. 710-717.View/Download from: Publisher's site
Behmanesh, R, Rahimi, I & Gandomi, AH 2020, 'Evolutionary Many-Objective Algorithms for Combinatorial Optimization Problems: A Comparative Study', Archives of Computational Methods in Engineering.View/Download from: Publisher's site
© 2020, CIMNE, Barcelona, Spain. Many optimization problems encountered in the real-world have more than two objectives. To address such optimization problems, a number of evolutionary many-objective optimization algorithms were developed recently. In this paper, we tested 18 evolutionary many-objective algorithms against well-known combinatorial optimization problems, including knapsack problem (MOKP), traveling salesman problem (MOTSP), and quadratic assignment problem (mQAP), all up to 10 objectives. Results show that some of the dominance and reference-based algorithms such as non-dominated sort genetic algorithm (NSGA-III), strength Pareto-based evolutionary algorithm based on reference direction (SPEA/R), and Grid-based evolutionary algorithm (GrEA) are promising algorithms to tackle MOKP and MOTSP with 5 and 10 while increasing the number of objectives. Also, the dominance-based algorithms such as MaOEA-DDFC as well as the indicator-based algorithms such as HypE are promising to solve mQAP with 5 and 10 objectives. In contrast, decomposition based algorithms present the best on almost problems at saving time. For example, t-DEA displayed superior performance on MOTSP for up to 10 objectives.
Chen, H, Heidari, AA, Chen, H, Wang, M, Pan, Z & Gandomi, AH 2020, 'Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies', Future Generation Computer Systems, vol. 111, pp. 175-198.View/Download from: Publisher's site
© 2020 Elsevier B.V. The first powerful variant of the Harris hawks optimization (HHO) is proposed in this work. HHO is a recently developed swarm-based stochastic algorithm that has previously shown excellent performance. In fact, the original HHO has features that can still be improved as it may experience convergence problems or may easily become trapped in local optima. To overcome these shortcomings of the original HHO, the first powerful variant of HHO integrates chaos strategy, topological multi-population strategy, and differential evolution (DE) strategy. For this, chaos mechanism is first introduced into the original algorithm to improve the exploitation propensities of HHO. The multi-population strategy with three mechanisms is embedded to augment the global search ability of the method. Finally, the DE mechanism is introduced into the HHO to enhance the quality of the solutions. Based on these well-regarded evolutionary mechanisms, we propose an enhanced DE-driven multi-population HHO (CMDHHO) algorithm. In this work, the proposed CMDHHO is compared with a range of other methods, including four original meta-heuristic algorithms, conventional HHO, twelve advanced algorithms based on IEEE CEC2017 benchmark functions, and IEEE CEC2011 real-world problems. Furthermore, the Friedman test and the non-parametric statistical Wilcoxon sign rank test are used to verify the significance of the results. The results of the experiments show that the three embedded mechanisms can effectively enhance the exploratory and exploitative traits of HHO. The time required for HHO to converge was substantially shortened. We suggest using the proposed CMDHHO as an effective tool to solve complex optimization problems.
Fani, A, Golroo, A, Ali Mirhassani, S & Gandomi, AH 2020, 'Pavement maintenance and rehabilitation planning optimisation under budget and pavement deterioration uncertainty', International Journal of Pavement Engineering.View/Download from: Publisher's site
© 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group. One of the key parts of a pavement management system is the maintenance and rehabilitation planning. The planning is usually developed under the assumption that all parameters are known with certainty. In practice, there are various parameters afflicted with large uncertainty. Ignoring the uncertainty may lead to a suboptimal plan adversely affecting the network conditions. The objective of this study is to develop an optimisation framework for network-level pavement maintenance and rehabilitation planning considering the uncertain nature of pavement deterioration and the budget with an applicable approach. A multistage stochastic mixed-integer programming model is proposed to find the optimal plan that is feasible for all possible scenarios of uncertainty and optimise the expectation of objective function. Two case studies of 4 and 21 pavement sections are presented to show the applicability of the proposed method. The value of stochastic solution and the expected value of perfect information which are the indices for evaluating the benefits of using the stochastic model are, respectively, 30% and 85% of the objective function of here and now model for the first case study and 26% and 42% of it regarding the second one. The indices are high indicating the effectiveness of the stochastic solution.
Faramarzi, A, Heidarinejad, M, Mirjalili, S & Gandomi, AH 2020, 'Marine Predators Algorithm: A nature-inspired metaheuristic', EXPERT SYSTEMS WITH APPLICATIONS, vol. 152.View/Download from: Publisher's site
© 2019, Springer Science+Business Media, LLC, part of Springer Nature. GPTIPS is a widely used genetic programming software that was developed in Matlab. The most recent version of this software, GPTIPS 2.0, provides a symbolic multi-gene regression for data analysis, in addition to traditional evolutionary algorithms. We briefly explain the GPTIPS methodology and describe its main features, including its weaknesses and strengths, and give examples of GPTIPS applications.
Gandomi, AH & Deb, K 2020, 'Implicit constraints handling for efficient search of feasible solutions', Computer Methods in Applied Mechanics and Engineering, vol. 363.View/Download from: Publisher's site
© 2020 Elsevier B.V. Real-world optimization problems usually involve constraints and sometimes even finding a single feasible solution is a challenging task. This study introduces a new approach for implicitly handling constraints. The proposed approach reduces the consideration of infeasible solutions by directly updating variable bounds with constraints, which is called the boundary update (BU) method. Two illustrative examples are used to explain the proposed approach, followed by applying it to mathematical and engineering constrained optimization problems. Finally, a surrogate-based problem and a large-dimensional and highly constrained problem are used to evaluate the BU method on these types of problems. The BU method is coupled with seven well-known evolutionary and mathematical optimization algorithms and the results show that the proposed BU method is a practical and effective approach and leads to better solutions with fewer function evaluations in nearly all cases, particularly for population-based optimization algorithms. This study should motivate optimization researchers and practitioners to pay more attention to the direct handling of constraints, rather than constraint handling by penalty or other fix-ups.
Govindarajan, P, Soundarapandian, RK, Gandomi, AH, Patan, R, Jayaraman, P & Manikandan, R 2020, 'Classification of stroke disease using machine learning algorithms', Neural Computing and Applications, vol. 32, pp. 817-828.View/Download from: Publisher's site
© 2019, Springer-Verlag London Ltd., part of Springer Nature. This paper presents a prototype to classify stroke that combines text mining tools and machine learning algorithms. Machine learning can be portrayed as a significant tracker in areas like surveillance, medicine, data management with the aid of suitably trained machine learning algorithms. Data mining techniques applied in this work give an overall review about the tracking of information with respect to semantic as well as syntactic perspectives. The proposed idea is to mine patients’ symptoms from the case sheets and train the system with the acquired data. In the data collection phase, the case sheets of 507 patients were collected from Sugam Multispecialty Hospital, Kumbakonam, Tamil Nadu, India. Next, the case sheets were mined using tagging and maximum entropy methodologies, and the proposed stemmer extracts the common and unique set of attributes to classify the strokes. Then, the processed data were fed into various machine learning algorithms such as artificial neural networks, support vector machine, boosting and bagging and random forests. Among these algorithms, artificial neural networks trained with a stochastic gradient descent algorithm outperformed the other algorithms with a higher classification accuracy of 95% and a smaller standard deviation of 14.69.
Jayawickreme, N, Atefi, E, Jayawickreme, E, Qin, J & Gandomi, AH 2020, 'Association rule learning is an easy and efficient method for identifying profiles of traumas and stressors that predict psychopathology in disaster survivors: The example of Sri Lanka', International Journal of Environmental Research and Public Health, vol. 17, no. 8.View/Download from: Publisher's site
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Research indicates that psychopathology in disaster survivors is a function of both experienced trauma and stressful life events. However, such studies are of limited utility to practitioners who are about to go into a new post-disaster setting as (1) most of them do not indicate which specific traumas and stressors are especially likely to lead to psychopathology; and (2) each disaster is characterized by its own unique traumas and stressors, which means that practitioners have to first collect their own data on common traumas, stressors and symptoms of psychopathology prior to planning any interventions. An easy-to-use and easy-to-interpret data analytical method that allows one to identify profiles of trauma and stressors that predict psychopathology would be of great utility to practitioners working in post-disaster contexts. We propose that association rule learning (ARL), a big data mining technique, is such a method. We demonstrate the technique by applying it to data from 337 survivors of the Sri Lankan civil war who completed the Penn/RESIST/Peradeniya War Problems Questionnaire (PRPWPQ), a comprehensive, culturally-valid measure of experienced trauma, stressful life events, anxiety and depression. ARL analysis revealed five profiles of traumas and stressors that predicted the presence of some anxiety, three profiles that predicted the presence of severe anxiety, four profiles that predicted the presence of some depression and five profiles that predicted the presence of severe depression. ARL allows one to identify context-specific associations between specific traumas, stressors and psychological distress, and can be of great utility to practitioners who wish to efficiently analyze data that they have collected, understand the output of that analysis, and use it to provide psychosocial aid to those who most need it in post-disaster settings.
Kashani, AR, Gandomi, M, Camp, CV & Gandomi, AH 2020, 'Optimum design of shallow foundation using evolutionary algorithms', Soft Computing, vol. 24, no. 9, pp. 6809-6833.View/Download from: Publisher's site
© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. In the current study, the performance of three evolutionary algorithms, differential algorithm (DE), evolution strategy (ES), and biogeography-based optimization algorithm (BBO), is examined for foundation design optimization. Moreover, four recent variations of evolutionary-based algorithms [i.e., improved differential evolution algorithm based on an adaptive mutation scheme, weighted differential evolution algorithm (WDE), linear population size reduction success-history-based adaptive differential evolution algorithm, and biogeography-based optimization with covariance matrix-based migration] have been tackled for handling the current problem. The objective function is based on the cost of shallow foundation designs that satisfy ACI 318-05 requirements is formulated as the objective function. This study addresses shallow footing optimization with two attitudes, routine optimization, and sensitivity analysis. As a further study, the effect of the location of the column at the top of the foundation is examined by adding two additional design variables. Three numerical case studies are used for both routine and sensitivity analysis. Moreover, the most common evolutionary-based technique, genetic algorithm (GA), is considered as a benchmark to evaluate the proposed methods’ efficiency. Based on the results, there is no algorithm which works as the most efficient solver over all the cases; while, BBO and WDE showed an acceptable performance because of satisfying records in most cases. There were several cases in which GA, DE, and ES were incapable of finding a valid solution which meets all the constraints simultaneously.
Khari, M, Garg, AK, Gandomi, AH, Gupta, R, Patan, R & Balusamy, B 2020, 'Securing Data in Internet of Things (IoT) Using Cryptography and Steganography Techniques', IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, vol. 50, no. 1, pp. 73-80.View/Download from: Publisher's site
Li, T, Fong, S, Li, X, Lu, Z & Gandomi, AH 2020, 'Swarm Decision Table and Ensemble Search Methods in Fog Computing Environment: Case of Day-Ahead Prediction of Building Energy Demands Using IoT Sensors', IEEE INTERNET OF THINGS JOURNAL, vol. 7, no. 3, pp. 2321-2342.View/Download from: Publisher's site
Manikandan, R, Patan, R, Gandomi, AH, Sivanesan, P & Kalyanaraman, H 2020, 'Hash polynomial two factor decision tree using IoT for smart health care scheduling', EXPERT SYSTEMS WITH APPLICATIONS, vol. 141.View/Download from: Publisher's site
Nagasubramanian, G, Sakthivel, RK, Patan, R, Gandomi, AH, Sankayya, M & Balusamy, B 2020, 'Securing e-health records using keyless signature infrastructure blockchain technology in the cloud', NEURAL COMPUTING & APPLICATIONS, vol. 32, no. 3, pp. 639-647.View/Download from: Publisher's site
Natarajan, S, Vairavasundaram, S, Natarajan, S & Gandomi, AH 2020, 'Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data', EXPERT SYSTEMS WITH APPLICATIONS, vol. 149.View/Download from: Publisher's site
Raghunathan, K, Soundarapandian, RK, Gandomi, AH, Ramachandran, M, Patan, R & Madda, RB 2020, 'Duo-Stage Decision: A Framework for Filling Missing Values, Consistency Check, and Repair of Decision Matrices in Multicriteria Group Decision Making', IEEE Transactions on Engineering Management.View/Download from: Publisher's site
IEEE With high uncertainty and vagueness in the decision-making process, maintaining consistency in the decision matrix is an open challenge. Previous studies on the intuitionistic fuzzy (IF) theory focused on the consistency of preference relation but ignored consistency of the decision matrix. In this article, efforts are made to propose a new duo-stage decision framework in the context of IF set to better circumvent the challenge. Often, decision makers (DMs) hesitate to provide certain values in the decision matrix that are filled randomly, resulting in inaccuracies in the decision-making process. To alleviate this issue, a new systematic procedure is developed that sensibly fills the missing data in the first stage. Following the first stage, consistency of the decision matrix is determined by extending Cronbach's alpha coefficient to IF context. Furthermore, efforts are made to repair inconsistent decision matrix iteratively. In the second stage, a new aggregation operator is presented for aggregation of DMs’ preferences. Also, a new mathematical model is proposed for criteria weight estimation, and a procedure is developed for ranking objects. The practical use of the proposed framework is demonstrated using a numerical example, and the strengths and weaknesses of the framework are investigated.
Rathee, M, Kumar, S, Gandomi, AH, Dilip, K, Balusamy, B & Patan, R 2020, 'Ant Colony Optimization Based Quality of Service Aware Energy Balancing Secure Routing Algorithm for Wireless Sensor Networks', IEEE Transactions on Engineering Management.View/Download from: Publisher's site
IEEE Existing routing protocols for wireless sensor networks (WSNs) focus primarily either on energy efficiency, quality of service (QoS), or security issues. However, a more holistic view of WSNs is needed, as many applications require both QoS and security guarantees along with the requirement of prolonging the lifetime of the network. The limited energy capacity of sensor nodes forces a tradeoff to be made between network lifetime, QoS, and security. To address these issues, an ant colony optimization based QoS aware energy balancing secure routing (QEBSR) algorithm for WSNs is proposed in this article. Improved heuristics for calculating the end-to-end delay of transmission and the trust factor of the nodes on the routing path are proposed. The proposed algorithm is compared with two existing algorithms: distributed energy balanced routing and energy efficient routing with node compromised resistance. Simulation results show that the proposed QEBSR algorithm performed comparatively better than the other two algorithms.
Sahoo, KS, Tiwary, M, Mishra, P, Reddy, SRS, Balusamy, B & Gandomi, AH 2020, 'Improving End-Users Utility in Software-Defined Wide Area Network Systems', IEEE Transactions on Network and Service Management.View/Download from: Publisher's site
IEEE Software Defined Networks (SDN) has brought a new form of network architecture that simplifies network management through innovations and programmability. But, the distributed control plane of SD-Wide Area Network is challenged by load imbalance problem due to the dynamic change of the traffic pattern. The packet_in messages are one of the major contributors of the control’s load. When such packet rate exceeds a certain threshold limit, the response time for control request increases non-linearly. In order to achieve better end-user experience, most of the previous works considered the optimal switch to controller association with an objective to minimize the response time on LAN environment but ignores the consequence of large scale network. In this regard, the proposed work realizes the necessity of layer-2 and layer-3 controller in LAN and WAN environment separately. A load prediction based alertness approach has been introduced to reduce the burden of the controllers. This approach may create an additional delay for the initial packets of the flow entry that lead to more prediction error. However, the proposed method reduces the error by selecting an optimal timeout value of the flow. Further, minimization of the response time between router to the controller has been taken care of. An extensive simulation shows the efficacy of the proposed scheme.
Samuel, P, Subbaiyan, S, Balusamy, B, Doraikannan, S & Gandomi, AH 2020, 'A Technical Survey on Intelligent Optimization Grouping Algorithms for Finite State Automata in Deep Packet Inspection', Archives of Computational Methods in Engineering.View/Download from: Publisher's site
© 2020, CIMNE, Barcelona, Spain. Construction and deployment of finite state automata from the regular expressions might results in huge overhead and results in the state explosion problem which is in need of large memory space, high bandwidth and additional computational time. To overcome this problem, a new framework is proposed, and several intelligent optimization algorithms are reviewed and compared in this framework. The proposed approach is called intelligent optimization grouping algorithms (IOGA), which intends to group regular expression intelligently. IOGAs are used to allocate the regular expression sets into various groups and to build independent deterministic finite automata (DFA) for each group. Grouping the regular expression efficiently solves the state explosion problem by achieving large-scale best tradeoff among memory utilization and computational time. This study reviews and compares the various alternatives of IOGA including genetic algorithm, ant colony optimization, particle swarm optimization, bacterial foraging optimization, artificial bee colony algorithm, biogeography based optimization, cuckoo search, firefly algorithm, bat algorithm and flower pollination algorithm for solving the problem of DFA state explosion and also for improving the overall efficiency of deep packet inspection (DPI). The discussions state that by effectively using these grouping algorithms along with DFA based DPI, the number of states can be reduced, providing a balance between the memory consumption, time complexity, throughput, inspection speed, convergence speed and grouping time.
© 2019 Elsevier B.V. In this study, new approximate solutions for consolidation have been developed in order to hasten the calculations. These solutions include two groups of equations, one can be used to calculate the average degree of consolidation and the other one for computing the time factor (inverse functions). Considering the complicated nature of consolidation, an evolutionary computation technique called Multi-Expression Programming was applied to generate several non-piecewise models which are accurate and straightforward enough for different purposes for calculating the degree of consolidation for each depth and its average as well for the whole soil layer. The parametric study was also performed to investigate the impact of each input parameter on the predicted consolidation degree of developed models for each depth. Moreover, the results of the consolidation test carried out on four different clays attained from the literature showed the proper performance of the proposed models.
© 2019, Springer-Verlag London Ltd., part of Springer Nature. Abstract: In the real world, we often come across conditions like optimization of more than one objective functions concurrently which are of conflicting nature and that makes the prospect of the problem more intricate. To overpower this contrasting state, an efficient meta-heuristic (MH) is required, which provides a balanced trade-off between diverging objective functions and gives an optimum set of solutions. In this article, a recently proposed MH called Heat Transfer Search (HTS) algorithm is enforced to elucidate the structural optimization problems with Multi-objective functions (described as MOHTS). MOHTS is an efficient MH which works on the principle of heat transfer and thermodynamics, where search agents are molecules which interact with other molecules and with surrounding through conduction, convection, and radiation modes of heat transfer. Five challenging benchmark problems of truss optimization have been taken into consideration here to examine the effectiveness of MOHTS. Procure results through the proposed method show the predominance over considered MHs. These benchmark problems are considered for discrete design variables for the structural optimization problem with two objectives, namely minimization of truss weight and maximization of nodal displacement. Here, the Pareto-optimal front achieved through computational experiments, in the process of optimization, is evaluated by three distinct performance quality indicators namely the Hypervolume, the Front spacing metric, and Inverted Generational Distance. Also, the obtained results after a number of runs are compared with other existing optimizers in the literature like multi-objective ant system, multi-objective ant colony system, and multi-objective symbiotic organism search, which manifest the superiority in the performance of the proposed algorithm over others. The statistical analysis of the experimental work has been carried...
Telikani, A, Gandomi, AH, Shahbahrami, A & Dehkordi, MN 2020, 'Privacy-preserving in association rule mining using an improved discrete binary artificial bee colony', EXPERT SYSTEMS WITH APPLICATIONS, vol. 144.View/Download from: Publisher's site
Akhani, M, Kashani, AR, Mousavi, M & Gandomi, AH 2019, 'A hybrid computational intelligence approach to predict spectral acceleration', MEASUREMENT, vol. 138, pp. 578-589.View/Download from: Publisher's site
Behera, TM, Mohapatra, SK, Samal, UC, Khan, MS, Daneshmand, M & Gandomi, AH 2019, 'Residual energy-based cluster-head selection in WSNs for IoT application', IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5132-5139.View/Download from: Publisher's site
© 2014 IEEE. Wireless sensor networks (WSNs) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head (CH) can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. This paper focuses on an efficient CH election scheme that rotates the CH position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy, and an optimum value of CHs to elect the next group of CHs for the network that suits for IoT applications, such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the low energy adaptive clustering hierarchy protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%.
Cheng, R, Omidvar, MN, Gandomi, AH, Sendhoff, B, Menzel, S & Yao, X 2019, 'Solving Incremental Optimization Problems via Cooperative Coevolution', IEEE Transactions on Evolutionary Computation, vol. 23, no. 5, pp. 762-775.View/Download from: Publisher's site
© 1997-2012 IEEE. Engineering designs can involve multiple stages, where at each stage, the design models are incrementally modified and optimized. In contrast to traditional dynamic optimization problems, where the changes are caused by some objective factors, the changes in such incremental optimization problems (IOPs) are usually caused by the modifications made by the decision makers during the design process. While existing work in the literature is mainly focused on traditional dynamic optimization, little research has been dedicated to solving such IOPs. In this paper, we study how to adopt cooperative coevolution to efficiently solve a specific type of IOPs, namely, those with increasing decision variables. First, we present a benchmark function generator on the basis of some basic formulations of IOPs with increasing decision variables and exploitable modular structure. Then, we propose a contribution-based cooperative coevolutionary framework coupled with an incremental grouping method for dealing with them. On one hand, the benchmark function generator is capable of generating various benchmark functions with various characteristics. On the other hand, the proposed framework is promising in solving such problems in terms of both optimization accuracy and computational efficiency. In addition, the proposed method is further assessed using a real-world application, i.e., the design optimization of a stepped cantilever beam.
Dhingra, S, Madda, RB, Gandomi, AH, Patan, R & Daneshmand, M 2019, 'Internet of things mobile-air pollution monitoring system (IoT-Mobair)', IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5577-5584.View/Download from: Publisher's site
© 2014 IEEE. Internet of Things (IoT) is a worldwide system of 'smart devices' that can sense and connect with their surroundings and interact with users and other systems. Global air pollution is one of the major concerns of our era. Existing monitoring systems have inferior precision, low sensitivity, and require laboratory analysis. Therefore, improved monitoring systems are needed. To overcome the problems of existing systems, we propose a three-phase air pollution monitoring system. An IoT kit was prepared using gas sensors, Arduino integrated development environment (IDE), and a Wi-Fi module. This kit can be physically placed in various cities to monitoring air pollution. The gas sensors gather data from air and forward the data to the Arduino IDE. The Arduino IDE transmits the data to the cloud via the Wi-Fi module. We also developed an Android application termed IoT-Mobair, so that users can access relevant air quality data from the cloud. If a user is traveling to a destination, the pollution level of the entire route is predicted, and a warning is displayed if the pollution level is too high. The proposed system is analogous to Google traffic or the navigation application of Google Maps. Furthermore, air quality data can be used to predict future air quality index (AQI) levels.
Doborjeh, M, Kasabov, N, Doborjeh, Z, Enayatollahi, R, Tu, E & Gandomi, AH 2019, 'Personalised modelling with spiking neural networks integrating temporal and static information.', Neural networks : the official journal of the International Neural Network Society, vol. 119, pp. 162-177.View/Download from: Publisher's site
This paper proposes a new personalised prognostic/diagnostic system that supports classification, prediction and pattern recognition when both static and dynamic/spatiotemporal features are presented in a dataset. The system is based on a proposed clustering method (named d2WKNN) for optimal selection of neighbouring samples to an individual with respect to the integration of both static (vector-based) and temporal individual data. The most relevant samples to an individual are selected to train a Personalised Spiking Neural Network (PSNN) that learns from sets of streaming data to capture the space and time association patterns. The generated time-dependant patterns resulted in a higher accuracy of classification/prediction (80% to 93%) when compared with global modelling and conventional methods. In addition, the PSNN models can support interpretability by creating personalised profiling of an individual. This contributes to a better understanding of the interactions between features. Therefore, an end-user can comprehend what interactions in the model have led to a certain decision (outcome). The proposed PSNN model is an analytical tool, applicable to several real-life health applications, where different data domains describe a person's health condition. The system was applied to two case studies: (1) classification of spatiotemporal neuroimaging data for the investigation of individual response to treatment and (2) prediction of risk of stroke with respect to temporal environmental data. For both datasets, besides the temporal data, static health data were also available. The hyper-parameters of the proposed system, including the PSNN models and the d2WKNN clustering parameters, are optimised for each individual.
Gandomi, AH, Daneshmand, M, Jha, R, Kaur, D, Ning, H, Robinson, C & Schilling, H 2019, 'Guest Editorial Nature-Inspired Approaches for IoT and Big Data', IEEE Internet of Things Journal, vol. 6, no. 6, pp. 9213-9216.View/Download from: Publisher's site
Gandomi, AH, Deb, K, Averill, RC, Rahnamayan, S & Omidvar, MN 2019, 'Using semi-independent variables to enhance optimization search', EXPERT SYSTEMS WITH APPLICATIONS, vol. 120, pp. 279-297.View/Download from: Publisher's site
Kashani, AR, Saneirad, A & Gandomi, AH 2019, 'Optimum design of reinforced earth walls using evolutionary optimization algorithms', Neural Computing and Applications.View/Download from: Publisher's site
© 2019, Springer-Verlag London Ltd., part of Springer Nature. This study addresses the optimum cost design of mechanically stabilized earth (MSE) using geosynthetics. The design process of MSEs is mathematically programmed based on an objective function depending on the length of reinforcements and vertical distance of reinforced layers. Design restrictions control the final design to be valid in terms of constraints. The aim is to explore the efficiency of evolutionary-based algorithms in dealing with MSE optimization problem along with automating the minimum cost design of MSE walls. To this end, three evolutionary algorithms, differential evolution (DE), evolution strategy, and biogeography-based optimization algorithm (BBO), are tackled to solve this problem. Comprehensive computational simulations confirm the impact of different effective parameters variation on the final design. Finally, the BBO algorithm performed the best, while DE recorded the most unsatisfactory results.
Kasinathan, G, Jayakumar, S, Gandomi, AH, Ramachandran, M, Fong, SJ & Patan, R 2019, 'Automated 3-D lung tumor detection and classification by an active contour model and CNN classifier', Expert Systems with Applications, vol. 134, pp. 112-119.View/Download from: Publisher's site
© 2019 Elsevier Ltd The World Health Organization (WHO) recently reported that the lung tumor was the leading cause of death worldwide. In this study, a practical computer-aided diagnosis (CAD) system is developed to increase a patient's chance of survival. Segmentation is acritical analysis tool for dividing a lung image into several sub-regions. This work characterized an automated 3-D lung segmentation tool modeled by an active contour model for computed tomography (CT) images. The proposed segmentation model is used to integrate the local image bias field formulation with the active contour model (ACM). Here, a local energy term is specified by using the mean squared error to reconcile severely in homogeneous CT images and used to detect and segment tumor regions efficiently with intensity inhomogeneity. In addition, a Multiscale Gaussian distribution was applied to the CT images for smoothening the evolution process, and features were determined. For proposed model evaluation, were used the Lung Image Database Consortium (LIDC-IDRI) data set that consisted of 850 lung nodule-lesion images that were segmented and refined to generate accurate 3D lesions of lung tumor CT images. Tumor portions were extracted with 97% accuracy. Using continuous feature extraction of 3-D images leads to attributing the deformation and quantifies the centroid displacement. In this work, predict the centroid displacement and contour points by a curve evolution method which results in more accurate predictions of contour changes and than the extracted images were classified using an Enhanced Convolutional Neural Network (CNN) Classifier. The experimental result shows that the modified Computer Aided Diagnosis (CAD) system has a high ability to acquire good accuracy and assures automated diagnosis of a lung tumor.
Krishnamurthi, R, Patan, R & Gandomi, AH 2019, 'Assistive pointer device for limb impaired people: A novel Frontier Point Method for hand movement recognition', Future Generation Computer Systems, vol. 98, pp. 650-659.View/Download from: Publisher's site
© 2019 Elsevier B.V. In this modern era, the use of computer technology and computing devices play significant role in every day human activities. From the disabled people perspective, there is huge demand to improve Human–Computer Interaction (HCI), to overcome their difficulty in using the standard interactive devices. Basically, HCI provides a way for humans to interact with a computer using a keyboard, a mouse, and other input devices in real-time. This paper proposes a novel assistive pointer device called Frontier Point method (FPM), which is based on a hand movement recognition technique. The proposed hand movement recognition technique primarily focuses on the direction of hand movement for dynamic recognition in real-time using least square fitting and virtual frame techniques. Next based on boundary values, such that if the hand crosses a boundary value of a given quadrant, then a SENDKEY stroke is generated that corresponds to that range. This method is implemented with the help of a depth sensor camera called Kinect. Kinect takes the RGB data and depth data of the human skeleton and generates coordinate information corresponding to specific body joints. Experiments were conducted in which different users were evaluated for their ability to navigate a PowerPoint presentation multiple times. Collectively, an average recognition time of 2.386 s was calculated with an average recognition rate of 97.37%.
Kumar, A, Ramachandran, M, Gandomi, AH, Patan, R, Lukasik, S & Soundarapandian, RK 2019, 'A deep neural network based classifier for brain tumor diagnosis', Applied Soft Computing Journal, vol. 82.View/Download from: Publisher's site
© 2019 Elsevier B.V. Classification process plays a key role in diagnosing brain tumors. Earlier research works are intended for identifying brain tumors using different classification techniques. However, the False Alarm Rates (FARs) of existing classification techniques are high. To improve the early-stage brain tumor diagnosis via classification the Weighted Correlation Feature Selection Based Iterative Bayesian Multivariate Deep Neural Learning (WCFS-IBMDNL) technique is proposed in this work. The WCFS-IBMDNL algorithm considers medical dataset for classifying the brain tumor diagnosis at an early stage. At first, the WCFS-IBMDNL technique performs Weighted Correlation-Based Feature Selection (WC-FS) by selecting subsets of medical features that are relevant for classification of brain tumors. After completing the feature selection process, the WCFS-IBMDNL technique uses Iterative Bayesian Multivariate Deep Neural Network (IBMDNN) classifier for reducing the misclassification error rate of brain tumor identification. The WCFS-IBMDNL technique was evaluated in JAVA language using Disease Diagnosis Rate (DDR), Disease Diagnosis Time (DDT), and FAR parameter through the epileptic seizure recognition dataset.
Mousavi, M, Holloway, D, Olivier, JC, Alavi, AH & Gandomi, AH 2019, 'A Shannon entropy approach for structural damage identification based on self-powered sensor data', Engineering Structures, vol. 200.View/Download from: Publisher's site
© 2019 Elsevier Ltd Piezo-floating-gate (PFG) sensors are a class of self-powered sensors fabricated using piezoelectric transducers and p-channel floating-gate metal-oxide-semiconductor (pMOS) transistors. These sensors are equipped with a series of floating-gates that are triggered when the voltage generated by the piezoelectric transducers exceeds one of the specified thresholds. Upon activation, the floating-gates cumulatively store the duration of the applied strain events. Defining optimal voltage thresholds plays a key role in the efficiency of the PFG sensors for structural damage identification. In this paper, symbolic dynamic analysis (SDA) based on Shannon entropy is used to find the effective voltage thresholds that ensure the maximum detectability of the structural damage-related changes. To this end, a baseline is constructed using the strain data obtained from the undamaged structure. These data are used to set the voltage threshold on every floating gate of the sensor. Then the posterior state of the structure is monitored using thresholds set up on the baseline and a cumulative density function (CDF) of strain events. In order to determine the damage severity, a damage index is defined based on the Euclidean norm of the distance between the CDFs for the damaged and healthy structure. The proposed technique is verified using experimental data for a steel plate subjected to an in-plane tension loading. The results confirm the capability of the proposed method in monitoring structures for damage initiation and/or propagation using the PFG sensors, and the CDFs on which the damage sensitive feature (DSF) is based can provide additional insights into the stress distributions.
Poongodi, T, Khan, MS, Patan, R, Gandomi, AH & Balusamy, B 2019, 'Robust Defense Scheme Against Selective Drop Attack in Wireless Ad Hoc Networks', IEEE Access, vol. 7, pp. 18409-18419.View/Download from: Publisher's site
© 2013 IEEE. Performance and security are two critical functions of wireless ad-hoc networks (WANETs). Network security ensures the integrity, availability, and performance of WANETs. It helps to prevent critical service interruptions and increases economic productivity by keeping networks functioning properly. Since there is no centralized network management in WANETs, these networks are susceptible to packet drop attacks. In selective drop attack, the neighboring nodes are not loyal in forwarding the messages to the next node. It is critical to identify the illegitimate node, which overloads the host node and isolating them from the network is also a complicated task. In this paper, we present a resistive to selective drop attack (RSDA) scheme to provide effective security against selective drop attack. A lightweight RSDA protocol is proposed for detecting malicious nodes in the network under a particular drop attack. The RSDA protocol can be integrated with the many existing routing protocols for WANETs such as AODV and DSR. It accomplishes reliability in routing by disabling the link with the highest weight and authenticate the nodes using the elliptic curve digital signature algorithm. In the proposed methodology, the packet drop rate, jitter, and routing overhead at a different pause time are reduced to 9%, 0.11%, and 45%, respectively. The packet drop rate at varying mobility speed in the presence of one gray hole and two gray hole nodes are obtained as 13% and 14% in RSDA scheme.
Sarveghadi, M, Gandomi, AH, Bolandi, H & Alavi, AH 2019, 'Development of prediction models for shear strength of SFRCB using a machine learning approach', Neural Computing and Applications, vol. 31, no. 7, pp. 2085-2094.View/Download from: Publisher's site
© 2015, The Natural Computing Applications Forum. In this study, new design equations were derived for the assessment of shear resistance of steel fiber-reinforced concrete beams (SFRCB) utilizing multi-expression programming (MEP). The superiority of MEP over conventional statistical techniques is due to its ability in modeling of mechanical behavior without a need to pre-define the model structure. The MEP models were developed using a comprehensive database obtained through an extensive literature review. New criteria were checked to verify the validity of the models. A sensitivity analysis was carried out and discussed. The MEP models provide good estimations of the shear strength of SFRCB. The developed models significantly outperform several equations found in the literature.
Sekaran, K, Khan, MS, Patan, R, Gandomi, AH, Krishna, PV & Kallam, S 2019, 'Improving the Response Time of M-Learning and Cloud Computing Environments Using a Dominant Firefly Approach', IEEE Access, vol. 7, pp. 30203-30212.View/Download from: Publisher's site
© 2013 IEEE. Mobile learning (m-learning) is a relatively new technology that helps students learn and gain knowledge using the Internet and Cloud computing technologies. Cloud computing is one of the recent advancements in the computing field that makes Internet access easy to end users. Many Cloud services rely on Cloud users for mapping Cloud software using virtualization techniques. Usually, the Cloud users' requests from various terminals will cause heavy traffic or unbalanced loads at the Cloud data centers and associated Cloud servers. Thus, a Cloud load balancer that uses an efficient load balancing technique is needed in all the cloud servers. We propose a new meta-heuristic algorithm, named the dominant firefly algorithm, which optimizes load balancing of tasks among the multiple virtual machines in the Cloud server, thereby improving the response efficiency of Cloud servers that concomitantly enhances the accuracy of m-learning systems. Our methods and findings used to solve load imbalance issues in Cloud servers, which will enhance the experiences of m-learning users. Specifically, our findings such as Cloud-Structured Query Language (SQL), querying mechanism in mobile devices will ensure users receive their m-learning content without delay; additionally, our method will demonstrate that by applying an effective load balancing technique would improve the throughput and the response time in mobile and cloud environments.
Selvaraj, A, Patan, R, Gandomi, AH, Deverajan, GG & Pushparaj, M 2019, 'Optimal virtual machine selection for anomaly detection using a swarm intelligence approach', APPLIED SOFT COMPUTING, vol. 84.View/Download from: Publisher's site
Tejani, GG, Pholdee, N, Bureerat, S, Prayogo, D & Gandomi, AH 2019, 'Structural optimization using multi-objective modified adaptive symbiotic organisms search', Expert Systems with Applications, vol. 125, pp. 425-441.View/Download from: Publisher's site
© 2019 Elsevier Ltd Multiple objective structural optimization is a challenging problem in which suitable optimization methods are needed to find optimal solutions. Therefore, to answer such problems effectively, a multi-objective modified adaptive symbiotic organisms search (MOMASOS) with two modified phases is planned along with a normal line method as an archiving technique for designing of structures. The proposed algorithm consists of two separate improved phases including adaptive mutualism and modified parasitism phases. The probabilistic nature of mutualism phase of MOSOS lets design variables to have higher exploration and higher exploitation simultaneously. As search advances, a stability between the global search and a local search has a significant effect on the solutions. Therefore, an adaptive mutualism phase is added to the offer MOASOS. Also, the parasitism phase of MOSOS offers over exploration which is a major issue of this phase. The over exploration results in higher computational cost since the majority of the new solutions gets rejected due to inferior objective functional values. In consideration of this issue, the parasitism phase is upgraded to a modified parasitism phase to increase the possibility of getting improved solutions. In addition, the proposed changes are comparatively simple and do not need an extra parameter setting for MOSOS. For the truss problems, mass minimization and maximization of nodal deflection are considered as objective functions, elemental stresses are considered as behavior constraints and (discrete) elemental sections are considered as side constraints. Five truss optimization problems validate the applicability of the considered meta-heuristics to solve complex engineering. Also, four constrained benchmark engineering design problems are solved to demonstrate the effectiveness of MOMASOS. The results confirmed that the proposed adaptive mutualism phase and modified parasitism phase with a normal line method a...
Wang, GG, Gandomi, AH, Alavi, AH & Gong, D 2019, 'A comprehensive review of krill herd algorithm: variants, hybrids and applications', Artificial Intelligence Review: an international survey and tutorial journal, vol. 51, no. 1, pp. 119-148.View/Download from: Publisher's site
© 2017, Springer Science+Business Media Dordrecht. Krill herd (KH) is a novel swarm-based metaheuristic optimization algorithm inspired by the krill herding behavior. The objective function in the KH optimization process is based on the least distance between the food location and position of a krill. The KH method has been proven to outperform several state-of-the-art metaheuristic algorithms on many benchmarks and engineering cases. This paper presents a comprehensive review of different versions of the KH algorithm and their engineering applications. The study is divided into the following general parts: KH variants, engineering optimization/application, and theoretical analysis. In addition, specific features of KH and future directions are discussed.
Zamani, H, Nadimi-Shahraki, MH & Gandomi, AH 2019, 'CCSA: Conscious Neighborhood-based Crow Search Algorithm for Solving Global Optimization Problems', Applied Soft Computing Journal, vol. 85.View/Download from: Publisher's site
© 2019 Elsevier B.V. In this paper, a conscious neighborhood-based crow search algorithm (CCSA) is proposed for solving global optimization and engineering design problems. It is a successful improvement to tackle the imbalance search strategy and premature convergence problems of the crow search algorithm. CCSA introduces three new search strategies called neighborhood-based local search (NLS), non-neighborhood based global search (NGS) and wandering around based search (WAS) in order to improve the movement of crows in different search spaces. Moreover, a neighborhood concept is defined to select the movement strategy between NLS and NGS consciously, which enhances the balance between local and global search. The proposed CCSA is evaluated on several benchmark functions and four applied problems of engineering design. In all experiments, CCSA is compared by other state-of-the-art swarm intelligence algorithms: CSA, BA, CLPSO, GWO, EEGWO, WOA, KH, ABC, GABC, and Best-so-far ABC. The experimental and statistical results show that CCSA is very competitive especially for large-scale optimization problems, and it is significantly superior to the compared algorithms. Furthermore, the proposed algorithm also finds the best optimal solution for the applied problems of engineering design.
Aslani, M, Ghasemi, P & Gandomi, AH 2018, 'Constrained mean-variance mapping optimization for truss optimization problems', STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS, vol. 27, no. 6.View/Download from: Publisher's site
Gandomi, AH & Alavi, AH 2018, 'Metaheuristics in Reliability and Risk Analysis', ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART A-CIVIL ENGINEERING, vol. 4, no. 3.View/Download from: Publisher's site
Gandomi, AH & Kashani, AR 2018, 'Automating pseudo-static analysis of concrete cantilever retaining wall using evolutionary algorithms', MEASUREMENT, vol. 115, pp. 104-124.View/Download from: Publisher's site
Gandomi, AH & Kashani, AR 2018, 'Construction Cost Minimization of Shallow Foundation Using Recent Swarm Intelligence Techniques', IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, vol. 14, no. 3, pp. 1099-1106.View/Download from: Publisher's site
Gandomi, AH & Kashani, AR 2018, 'Probabilistic evolutionary bound constraint handling for particle swarm optimization', OPERATIONAL RESEARCH, vol. 18, no. 3, pp. 801-823.View/Download from: Publisher's site
Gharehbaghi, S, Gandomi, AH, Achakpour, S & Omidvar, MN 2018, 'A hybrid computational approach for seismic energy demand prediction', EXPERT SYSTEMS WITH APPLICATIONS, vol. 110, pp. 335-351.View/Download from: Publisher's site
Kurugodu, H, Bordoloi, S, Hong, Y, Garg, A, Garg, A, Sreedeep, S & Gandomi, AH 2018, 'Genetic programming for soil-fiber composite assessment', ADVANCES IN ENGINEERING SOFTWARE, vol. 122, pp. 50-61.View/Download from: Publisher's site
Mousavi, M & Gandomi, AH 2018, 'An input-output damage detection method using static equivalent formulation of dynamic vibration', ARCHIVES OF CIVIL AND MECHANICAL ENGINEERING, vol. 18, no. 2, pp. 508-514.View/Download from: Publisher's site
Tahmassebi, A, Gandomi, AH, Fong, S, Meyer-Baese, A & Foo, SY 2018, 'Multi-stage optimization of a deep model: A case study on ground motion modeling', PLOS ONE, vol. 13, no. 9.View/Download from: Publisher's site
Tahmassebi, A, Gandomi, AH, Schulte, MHJ, Goudriaan, AE, Foo, SY & Meyer-Baese, A 2018, 'Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification', COMPLEXITY.View/Download from: Publisher's site
Abualigah, LM, Khader, AT, Hanandeh, ES & Gandomi, AH 2017, 'A novel hybridization strategy for krill herd algorithm applied to clustering techniques', APPLIED SOFT COMPUTING, vol. 60, pp. 423-435.View/Download from: Publisher's site
Ahmadi, M, Naderpour, H, Kheyroddin, A & Gandomi, AH 2017, 'Seismic Failure Probability and Vulnerability Assessment of Steel-Concrete Composite Structures', PERIODICA POLYTECHNICA-CIVIL ENGINEERING, vol. 61, no. 4, pp. 939-950.View/Download from: Publisher's site
Babanajad, SK, Gandomi, AH & Alavi, AH 2017, 'New prediction models for concrete ultimate strength under true-triaxial stress states: An evolutionary approach', ADVANCES IN ENGINEERING SOFTWARE, vol. 110, pp. 55-68.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH, Gandomi, M & Kazemi, S 2017, 'Formulation of shear strength of slender RC beams using gene expression programming, part II: With shear reinforcement', MEASUREMENT, vol. 95, pp. 367-376.View/Download from: Publisher's site
Gandomi, AH, Kashani, AR & Zeighami, F 2017, 'Retaining wall optimization using interior search algorithm with different bound constraint handling', INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, vol. 41, no. 11, pp. 1304-1331.View/Download from: Publisher's site
Gandomi, AH, Kashani, AR, Mousavi, M & Jalalvandi, M 2017, 'Slope stability analysis using evolutionary optimization techniques', INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, vol. 41, no. 2, pp. 251-264.View/Download from: Publisher's site
Gandomi, AH, Kashani, AR, Roke, DA & Mousavi, M 2017, 'Optimization of retaining wall design using evolutionary algorithms', STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, vol. 55, no. 3, pp. 809-825.View/Download from: Publisher's site
Gholampour, A, Gandomi, AH & Ozbakkaloglu, T 2017, 'New formulations for mechanical properties of recycled aggregate concrete using gene expression programming', CONSTRUCTION AND BUILDING MATERIALS, vol. 130, pp. 122-145.View/Download from: Publisher's site
Kiani, B, Sajedi, S, Gandomi, AH, Huang, Q & Liang, RY 2017, 'Optimal adjustment of ACI formula for shrinkage of concrete containing pozzolans', CONSTRUCTION AND BUILDING MATERIALS, vol. 131, pp. 485-495.View/Download from: Publisher's site
Mirjalili, S, Gandomi, AH, Mirjalili, SZ, Saremi, S, Faris, H & Mirjalili, SM 2017, 'Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems', ADVANCES IN ENGINEERING SOFTWARE, vol. 114, pp. 163-191.View/Download from: Publisher's site
Sajedi, S, Huang, Q, Gandomi, AH & Kiani, B 2017, 'Reliability-Based Multiobjective Design Optimization of Reinforced Concrete Bridges Considering Corrosion Effect', ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART A-CIVIL ENGINEERING, vol. 3, no. 3.View/Download from: Publisher's site
Shahrara, N, Celik, T & Gandomi, AH 2017, 'GENE EXPRESSION PROGRAMMING APPROACH TO COST ESTIMATION FORMULATION FOR UTILITY PROJECTS', JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, vol. 23, no. 1, pp. 85-95.View/Download from: Publisher's site
Shahrara, N, Celik, T & Gandomi, AH 2017, 'RISK ANALYSIS OF BOT CONTRACTS USING SOFT COMPUTING', JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, vol. 23, no. 2, pp. 232-240.View/Download from: Publisher's site
Asghari, A & Gandomi, AH 2016, 'Ductility reduction factor and collapse mechanism evaluation of a new steel knee braced frame', STRUCTURE AND INFRASTRUCTURE ENGINEERING, vol. 12, no. 2, pp. 239-255.View/Download from: Publisher's site
Gandomi, AH & Alavi, AH 2016, 'AN INTRODUCTION OF KRILL HERD ALGORITHM FOR ENGINEERING OPTIMIZATION', JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, vol. 22, no. 3, pp. 302-310.View/Download from: Publisher's site
Gandomi, AH, Sajedi, S, Kiani, B & Huang, Q 2016, 'Genetic programming for experimental big data mining: A case study on concrete creep formulation', AUTOMATION IN CONSTRUCTION, vol. 70, pp. 89-97.View/Download from: Publisher's site
Gandomi, M, Soltanpour, M, Zolfaghari, MR & Gandomi, AH 2016, 'Prediction of peak ground acceleration of Iran's tectonic regions using a hybrid soft computing technique', GEOSCIENCE FRONTIERS, vol. 7, no. 1, pp. 75-82.View/Download from: Publisher's site
Kashani, AR, Gandomi, AH & Mousavi, M 2016, 'Imperialistic Competitive Algorithm: A metaheuristic algorithm for locating the critical slip surface in 2-Dimensional soil slopes', GEOSCIENCE FRONTIERS, vol. 7, no. 1, pp. 83-89.View/Download from: Publisher's site
Kiani, B, Gandomi, AH, Sajedi, S & Liang, RY 2016, 'New Formulation of Compressive Strength of Preformed-Foam Cellular Concrete: An Evolutionary Approach', JOURNAL OF MATERIALS IN CIVIL ENGINEERING, vol. 28, no. 10.View/Download from: Publisher's site
Mousavi, M & Gandomi, AH 2016, 'A hybrid damage detection method using dynamic-reduction transformation matrix and modal force error', ENGINEERING STRUCTURES, vol. 111, pp. 425-434.View/Download from: Publisher's site
Wang, G-G, Deb, S, Gandomi, AH & Alavi, AH 2016, 'Opposition-based krill herd algorithm with Cauchy mutation and position clamping', NEUROCOMPUTING, vol. 177, pp. 147-157.View/Download from: Publisher's site
Wang, G-G, Gandomi, AH, Alavi, AH & Deb, S 2016, 'A hybrid method based on krill herd and quantum-behaved particle swarm optimization', NEURAL COMPUTING & APPLICATIONS, vol. 27, no. 4, pp. 989-1006.View/Download from: Publisher's site
Wang, G-G, Gandomi, AH, Alavi, AH & Deb, S 2016, 'A Multi-Stage Krill Herd Algorithm for Global Numerical Optimization', INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS, vol. 25, no. 2.View/Download from: Publisher's site
Wang, GG, Gandomi, AH, Yang, XS & Alavi, AH 2016, 'A new hybrid method based on krill herd and cuckoo search for global optimisation tasks', International Journal of Bio-Inspired Computation, vol. 8, no. 5, pp. 286-299.View/Download from: Publisher's site
©2016 Inderscience Enterprises Ltd. Recently, Gandomi and Alavi proposed a new heuristic search method, called krill herd (KH), for solving global optimisation problems. In order to make KH more effective, a hybrid meta-heuristic cuckoo search and krill herd (CSKH) method is proposed for function optimisation. The CSKH introduces krill updating (KU) and krill abandoning (KA) operator originated from cuckoo search (CS) during the process when the krill updating so as to greatly enhance its effectiveness and reliability dealing with numerical optimisation problems. The KU operator inspires the intensive exploitation and allows the krill individuals implement a careful search in the later run phase of the search, while KA operator is used to further enhance the exploration of the CSKH in place of a fraction of the worse krill at the end of each generation. The effectiveness of these improvements is tested by 14 standard benchmarking functions and experimental results show, in most cases, this hybrid meta-heuristic CSKH algorithm is more effective and efficient than the original KH and other approaches.
Wang, G-G, Gandomi, AH, Yang, X-S & Alavi, AH 2016, 'A new hybrid method based on krill herd and cuckoo search for global optimisation tasks', INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION, vol. 8, no. 5, pp. 286-299.View/Download from: Publisher's site
Wang, G-G, Gandomi, AH, Zhao, X & Chu, HCE 2016, 'Hybridizing harmony search algorithm with cuckoo search for global numerical optimization', SOFT COMPUTING, vol. 20, no. 1, pp. 273-285.View/Download from: Publisher's site
Gandomi, AH & Roke, DA 2015, 'Assessment of artificial neural network and genetic programming as predictive tools', ADVANCES IN ENGINEERING SOFTWARE, vol. 88, pp. 63-72.View/Download from: Publisher's site
Gandomi, AH & Yun, GJ 2015, 'Coupled SelfSim and genetic programming for non- linear material constitutive modelling', INVERSE PROBLEMS IN SCIENCE AND ENGINEERING, vol. 23, no. 7, pp. 1101-1119.View/Download from: Publisher's site
Gandomi, AH, Faramarzifar, A, Ghanad Rezaee, P, Asghari, A & Talatahari, S 2015, 'New design equations for elastic modulus of concrete usingmulti expression programming', JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, vol. 21, no. 6, pp. 761-774.View/Download from: Publisher's site
Gandomi, AH, Kashani, AR, Mousavi, M & Jalalvandi, M 2015, 'Slope stability analyzing using recent swarm intelligence techniques', INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, vol. 39, no. 3, pp. 295-309.View/Download from: Publisher's site
Gandomi, AH, Kashani, AR, Roke, DA & Mousavi, M 2015, 'Optimization of retaining wall design using recent swarm intelligence techniques', ENGINEERING STRUCTURES, vol. 103, pp. 72-84.View/Download from: Publisher's site
Hosseini, SSS, Yang, XS, Gandomi, AH & Nemati, A 2015, 'Solutions of non-smooth economic dispatch problems by swarm intelligence', Adaptation, Learning, and Optimization, vol. 18, pp. 129-146.View/Download from: Publisher's site
© Springer International Publishing Switzerland 2015. The increasing costs of fuels and operations of power generating units necessitate the development of optimization methods for economic dispatch (ED) problems. Classical optimization techniques such as direct search and gradient methods often fail to find global optimum solutions. Modern optimization techniques are often meta-heuristic, and they are very promising in solving nonlinear programming problems. This chapter presents a novel method to determine the feasible optimal solutions of the ED problems utilizing the newly developed Bat Algorithm (BA). The proposed BA is based on the echolocation behavior of bats. This technique is adapted to solve non-convex ED problems under different nonlinear constraints such as transmission losses, ramp rate limits, multi-fuel options and prohibited operating zones. Parameters are tuned to give the best results for these problems. To describe the efficiency and applicability of the proposed algorithm, we will use four ED test systems with non-convexity. We will compare our results with some of the most recently published ED solution methods. Comparing with the other existing techniques, the proposed approach can find better solutions than other methods. This method can be deemed to be a promising alternative for solving the ED problems in real systems.
Kennedy, MJ, Gandomi, AH & Miller, CM 2015, 'Coagulation modeling using artificial neural networks to predict both turbidity and DOM-PARAFAC component removal', Journal of Environmental Chemical Engineering, vol. 3, no. 4, pp. 2829-2838.View/Download from: Publisher's site
© 2015 Elsevier Ltd. In this study, four different neural network models were evaluated for predicting both turbidity and dissolved organic matter (DOM) removal during the coagulation process at the Akron Water Treatment Plant (Akron, Ohio, USA). DOM was monitored and characterized using fluorescence spectroscopy and parallel factor (PARAFAC) analysis, building upon previous research which identified three unique fluorescence components (C1, C2, and C3). Neural network models were built using operational data to predict each of the fluorescence components and turbidity after coagulation based on variable raw water quality and chemical doses. Correlation coefficients between measured and model predicted values for the final turbidity, C1, C2, and C3 models on an unseen test data set were 0.91, 0.95, 0.97, and 0.51, respectively. The predictive capability of the top performing model for each parameter was evaluated using parametric analysis, external validation criteria, and the absolute relative error distribution. Results suggest that the models for settled turbidity and the three settled component scores are valid and can be used to predict the removal of individual fractions of DOM (as measured by PARAFAC components) as a function of chemical dose and raw water quality, providing the water plant the ability to simultaneously manage two key water quality treatment objectives.
Mirzahosseini, M, Najjar, YM, Alavi, AH & Gandomi, AH 2015, 'Next-Generation Models for Evaluation of the Flow Number of Asphalt Mixtures', INTERNATIONAL JOURNAL OF GEOMECHANICS, vol. 15, no. 6.View/Download from: Publisher's site
Talatahari, S, Gandomi, AH, Yang, X-S & Deb, S 2015, 'Optimum design of frame structures using the Eagle Strategy with Differential Evolution', ENGINEERING STRUCTURES, vol. 91, pp. 16-25.View/Download from: Publisher's site
Wang, GG, Gandomi, AH & Alavi, AH 2015, 'Study of lagrangian and evolutionary parameters in krill herd algorithm', Adaptation, Learning, and Optimization, vol. 18, pp. 111-128.View/Download from: Publisher's site
© 2015, Springer International Publishing Switzerland. Krill Herd (KH) is a novel swarm-based intelligent optimization method developed through the idealization of the krill swarm. In the basic KH method, all the movement parameters used are originated from real nature-driven data found in the literature. The parameter setting based on such data is not necessarily the best selection. In this work, a systematic method is presented for the selection of the best parameter setting for the KH algorithm through an extensive study of arrays of high-dimensional benchmark problems. An important finding is that the best performance of KH can be obtained by setting effective coefficient of the krill individual (Cbest), food coefficient (Cfood), maximum diffusion speed (Dmax), crossover probability (Cr) and mutation probability (Mu) parameters to 4.00, 4.25, 0.014, 0.225, and 0.025, respectively. This finding would eliminate the concerns regarding the optimal tuning of the KH algorithm for its most future applications.
Bagheri, M, Borhani, TNG, Gandomi, AH & Manan, ZA 2014, 'A simple modelling approach for prediction of standard state real gas entropy of pure materials', SAR AND QSAR IN ENVIRONMENTAL RESEARCH, vol. 25, no. 9, pp. 695-710.View/Download from: Publisher's site
Bayazidi, AM, Wang, G-G, Bolandi, H, Alavi, AH & Gandomi, AH 2014, 'Multigene Genetic Programming for Estimation of Elastic Modulus of Concrete', MATHEMATICAL PROBLEMS IN ENGINEERING, vol. 2014.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH, Asghari, A, Niroomand, H & Nazar, AM 2014, 'An innovative approach for modeling of hysteretic energy demand in steel moment resisting frames', NEURAL COMPUTING & APPLICATIONS, vol. 24, no. 6, pp. 1285-1291.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH, Kazemi, S & Gandomi, M 2014, 'Formulation of shear strength of slender RC beams using gene expression programming, part I: Without shear reinforcement', AUTOMATION IN CONSTRUCTION, vol. 42, pp. 112-121.View/Download from: Publisher's site
Gandomi, AH, Mohammadzadeh, DS, Luis Perez-Ordonez, J & Alavi, AH 2014, 'Linear genetic programming for shear strength prediction of reinforced concrete beams without stirrups', APPLIED SOFT COMPUTING, vol. 19, pp. 112-120.View/Download from: Publisher's site
Guo, L, Wang, G-G, Gandomi, AH, Alavi, AH & Duan, H 2014, 'A new improved krill herd algorithm for global numerical optimization', NEUROCOMPUTING, vol. 138, pp. 392-402.View/Download from: Publisher's site
Talatahari, E, Talatahari, S, Gandomi, AH & Yang, XS 2014, 'Advances of Swarm Intelligent Systems in Gene Expression Data Classification', JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING, vol. 22, no. 3, pp. 307-315.
Talatahari, S, Gandomi, AH & Yun, GJ 2014, 'Optimum design of tower structures using Firefly Algorithm', STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS, vol. 23, no. 5, pp. 350-361.View/Download from: Publisher's site
Wang, G-G, Gandomi, AH & Alavi, AH 2014, 'An effective krill herd algorithm with migration operator in biogeography-based optimization', APPLIED MATHEMATICAL MODELLING, vol. 38, no. 9-10, pp. 2454-2462.View/Download from: Publisher's site
Wang, G-G, Gandomi, AH, Alavi, AH & Hao, G-S 2014, 'Hybrid krill herd algorithm with differential evolution for global numerical optimization', NEURAL COMPUTING & APPLICATIONS, vol. 25, no. 2, pp. 297-308.View/Download from: Publisher's site
Wang, G-G, Gandomi, AH, Yang, X-S & Alavi, AH 2014, 'A novel improved accelerated particle swarm optimization algorithm for global numerical optimization', ENGINEERING COMPUTATIONS, vol. 31, no. 7, pp. 1198-1220.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH, Nejad, HC, Mollahasani, A & Rashed, A 2013, 'Design equations for prediction of pressuremeter soil deformation moduli utilizing expression programming systems', NEURAL COMPUTING & APPLICATIONS, vol. 23, no. 6, pp. 1771-1786.View/Download from: Publisher's site
Aminian, P, Niroomand, H, Gandomi, AH, Alavi, AH & Esmaeili, MA 2013, 'New design equations for assessment of load carrying capacity of castellated steel beams: a machine learning approach', NEURAL COMPUTING & APPLICATIONS, vol. 23, no. 1, pp. 119-131.View/Download from: Publisher's site
Babanajad, SK, Gandomi, AH, Mohammadzadeh S, D & Alavi, AH 2013, 'Numerical modeling of concrete strength under multiaxial confinement pressures using linear genetic programming', AUTOMATION IN CONSTRUCTION, vol. 36, pp. 136-144.View/Download from: Publisher's site
Bagheri, M, Gandomi, AH, Bagheri, M & Shahbaznezhad, M 2013, 'Multi-expression programming based model for prediction of formation enthalpies of nitro-energetic materials', EXPERT SYSTEMS, vol. 30, no. 1, pp. 66-78.View/Download from: Publisher's site
Gandomi, A & Zolfaghari, S 2013, 'Profitability of loyalty reward programs: An analytical investigation', OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, vol. 41, no. 4, pp. 797-807.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH, Shadmehri, DM & Sahab, MG 2013, 'An empirical model for shear capacity of RC deep beams using genetic-simulated annealing', ARCHIVES OF CIVIL AND MECHANICAL ENGINEERING, vol. 13, no. 3, pp. 354-369.View/Download from: Publisher's site
Gandomi, AH, Talatahari, S, Tadbiri, F & Alavi, AH 2013, 'Krill herd algorithm for optimum design of truss structures', INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION, vol. 5, no. 5, pp. 281-288.View/Download from: Publisher's site
Gandomi, AH, Talatahari, S, Yang, X-S & Deb, S 2013, 'Design optimization of truss structures using cuckoo search algorithm', STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS, vol. 22, no. 17, pp. 1330-1349.View/Download from: Publisher's site
Gandomi, AH, Yang, X-S & Alavi, AH 2013, 'Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems', ENGINEERING WITH COMPUTERS, vol. 29, no. 1, pp. 17-35.View/Download from: Publisher's site
Gandomi, AH, Yang, X-S & Alavi, AH 2013, 'Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems (vol 29, pg 17, 2013)', ENGINEERING WITH COMPUTERS, vol. 29, no. 2, pp. 245-245.View/Download from: Publisher's site
Gandomi, AH, Yang, X-S, Alavi, AH & Talatahari, S 2013, 'Bat algorithm for constrained optimization tasks', NEURAL COMPUTING & APPLICATIONS, vol. 22, no. 6, pp. 1239-1255.View/Download from: Publisher's site
Gandomi, AH, Yang, X-S, Talatahari, S & Alavi, AH 2013, 'Firefly algorithm with chaos', COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, vol. 18, no. 1, pp. 89-98.View/Download from: Publisher's site
Gandomi, AH, Yun, GJ & Alavi, AH 2013, 'An evolutionary approach for modeling of shear strength of RC deep beams', MATERIALS AND STRUCTURES, vol. 46, no. 12, pp. 2109-2119.View/Download from: Publisher's site
Gandomi, AH, Yun, GJ, Yang, X-S & Talatahari, S 2013, 'Chaos-enhanced accelerated particle swarm optimization', COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, vol. 18, no. 2, pp. 327-340.View/Download from: Publisher's site
Mousavi, SM, Alavi, AH, Mollahasani, A, Gandomi, AH & Esmaeili, MA 2013, 'Formulation of soil angle of shearing resistance using a hybrid GP and OLS method', ENGINEERING WITH COMPUTERS, vol. 29, no. 1, pp. 37-53.View/Download from: Publisher's site
Talatahari, S, Kheirollahi, M, Farahmandpour, C & Gandomi, AH 2013, 'A multi-stage particle swarm for optimum design of truss structures', NEURAL COMPUTING & APPLICATIONS, vol. 23, no. 5, pp. 1297-1309.View/Download from: Publisher's site
Wang, G-G, Gandomi, AH & Alavi, AH 2013, 'A chaotic particle-swarm krill herd algorithm for global numerical optimization', KYBERNETES, vol. 42, no. 6, pp. 962-978.View/Download from: Publisher's site
Yun, GJ, Rahimi, MR, Gandomi, AH, Lim, G-C & Choi, J-S 2013, 'Stress sensing performance using mechanoluminescence of SrAl2O4:Eu (SAOE) and SrAl2O4:Eu, Dy (SAOED) under mechanical loadings', SMART MATERIALS AND STRUCTURES, vol. 22, no. 5.View/Download from: Publisher's site
Alavi, AH & Gandomi, AH 2012, 'Discussion on "Models to predict the deformation modulus and the coefficient of subgrade reaction for earth filling structures" by Ismail Dincer [Adv. Eng. Software 42 (2011) 160-171]', ADVANCES IN ENGINEERING SOFTWARE, vol. 52, pp. 44-46.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH & Mousavi, SM 2012, 'Discussion on "Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods"', ENGINEERING GEOLOGY, vol. 137, pp. 107-108.View/Download from: Publisher's site
Alavi, AH, Mollahasani, A, Gandomi, AH & Bazaz, JB 2012, 'Formulation of secant and reloading soil deformation moduli using multi expression programming', ENGINEERING COMPUTATIONS, vol. 29, no. 1-2, pp. 173-197.View/Download from: Publisher's site
Asghari, A, Mirghaderi, R & Gandomi, AH 2012, 'Determination of ultimate load and possible failure path for a rigid strip footing on soil partially supported by retaining wall using an adaptive refinement process', International Journal of Mathematical Modelling and Numerical Optimisation, vol. 3, no. 3, pp. 210-230.View/Download from: Publisher's site
In most branches of soil mechanics and foundation engineering, approximate methods are often used for computing the ultimate load of a footing and slope stability analysis. There are different types of approximate methods. Such as slip-line method, limit equilibrium and limit analysis. Each of the mentioned methods has restrictions in the displacement computing values, failure path, simultaneously satisfying equilibrium equations, fundamental relationships and compatibility equations, and access to the real failure mechanism. Finite element method can be used to overcome some of these restrictions. But finite element method itself has weaknesses. Some of these weaknesses are lack of awareness of suitable element sizes, lack of awareness of error value in analysis process, lack of accurate information about failure path development and costly access to failure mechanism path occurrence. Here, the adaptive refinement based on the rule of gradient recovery and concept of norm was presented as a powerful method for overcoming the restrictions of the general finite element method. This paper also tries to find an access to both the propagation of failure path and the ultimate load value for a rigid strip footing on soil partially supported by a retaining wall. Copyright © 2012 Inderscience Enterprises Ltd.
Bagheri, M, Bagheri, M, Gandomi, AH & Golbraikh, A 2012, 'Simple yet accurate prediction method for sublimation enthalpies of organic contaminants using their molecular structure', THERMOCHIMICA ACTA, vol. 543, pp. 96-106.View/Download from: Publisher's site
Gandomi, AH & Alavi, AH 2012, 'A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems', NEURAL COMPUTING & APPLICATIONS, vol. 21, no. 1, pp. 171-187.View/Download from: Publisher's site
Gandomi, AH & Alavi, AH 2012, 'A new multi-gene genetic programming approach to non-linear system modeling. Part II: geotechnical and earthquake engineering problems', NEURAL COMPUTING & APPLICATIONS, vol. 21, no. 1, pp. 189-201.View/Download from: Publisher's site
Gandomi, AH & Alavi, AH 2012, 'Krill herd: A new bio-inspired optimization algorithm', COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, vol. 17, no. 12, pp. 4831-4845.View/Download from: Publisher's site
Gandomi, AH, Babanajad, SK, Alavi, AH & Farnam, Y 2012, 'Novel Approach to Strength Modeling of Concrete under Triaxial Compression', JOURNAL OF MATERIALS IN CIVIL ENGINEERING, vol. 24, no. 9, pp. 1132-1143.View/Download from: Publisher's site
Gandomi, AH, Yang, X-S, Talatahari, S & Deb, S 2012, 'Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization', COMPUTERS & MATHEMATICS WITH APPLICATIONS, vol. 63, no. 1, pp. 191-200.View/Download from: Publisher's site
Hosseini, SSS & Gandomi, AH 2012, 'Short-term load forecasting of power systems by gene expression programming', NEURAL COMPUTING & APPLICATIONS, vol. 21, no. 2, pp. 377-389.View/Download from: Publisher's site
Mollahasani, A, Alavi, AH & Gandomi, AH 2012, 'Reply to Comments on "Empirical modelling of plate load test moduli of soil via gene expression programming" by Ali Mollahasani, Amir Hossein Alavi, Amir Hossein Gandomi [Computers and Geotechnics 38 (2011) 281-286]', COMPUTERS AND GEOTECHNICS, vol. 39, pp. 73-74.View/Download from: Publisher's site
Mousavi, SM, Aminian, P, Gandomi, AH, Alavi, AH & Bolandi, H 2012, 'A new predictive model for compressive strength of HPC using gene expression programming', ADVANCES IN ENGINEERING SOFTWARE, vol. 45, no. 1, pp. 105-114.View/Download from: Publisher's site
Talatahari, S, Azar, BF, Sheikholeslami, R & Gandomi, AH 2012, 'Imperialist competitive algorithm combined with chaos for global optimization', COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, vol. 17, no. 3, pp. 1312-1319.View/Download from: Publisher's site
Yang, XS & Gandomi, AH 2012, 'Bat algorithm: A novel approach for global engineering optimization', Engineering Computations (Swansea, Wales), vol. 29, no. 5, pp. 464-483.View/Download from: Publisher's site
Purpose - Nature-inspired algorithms are among the most powerful algorithms for optimization. The purpose of this paper is to introduce a new nature-inspired metaheuristic optimization algorithm, called bat algorithm (BA), for solving engineering optimization tasks. Design/methodology/approach - The proposed BA is based on the echolocation behavior of bats. After a detailed formulation and explanation of its implementation, BA is verified using eight nonlinear engineering optimization problems reported in the specialized literature. Findings - BA has been carefully implemented and carried out optimization for eight well-known optimization tasks; then a comparison has been made between the proposed algorithm and other existing algorithms. Originality/value - The optimal solutions obtained by the proposed algorithm are better than the best solutions obtained by the existing methods. The unique search features used in BA are analyzed, and their implications for future research are also discussed in detail. © Emerald Group Publishing Limited.
Yang, X-S, Hosseini, SSS & Gandomi, AH 2012, 'Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect', APPLIED SOFT COMPUTING, vol. 12, no. 3, pp. 1180-1186.View/Download from: Publisher's site
Zargari, SA, Siabil, SZ, Alavi, AH & Gandomi, AH 2012, 'A computational intelligence-based approach for short-term traffic flow prediction', EXPERT SYSTEMS, vol. 29, no. 2, pp. 124-142.View/Download from: Publisher's site
Alavi, AH & Gandomi, AH 2011, 'A robust data mining approach for formulation of geotechnical engineering systems', ENGINEERING COMPUTATIONS, vol. 28, no. 3-4, pp. 242-274.View/Download from: Publisher's site
Alavi, AH & Gandomi, AH 2011, 'Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing', COMPUTERS & STRUCTURES, vol. 89, no. 23-24, pp. 2176-2194.View/Download from: Publisher's site
Alavi, AH, Ameri, M, Gandomi, AH & Mirzahosseini, MR 2011, 'Formulation of flow number of asphalt mixes using a hybrid computational method', CONSTRUCTION AND BUILDING MATERIALS, vol. 25, no. 3, pp. 1338-1355.View/Download from: Publisher's site
Alavi, AH, Aminian, P, Gandomi, AH & Esmaeili, MA 2011, 'Genetic-based modeling of uplift capacity of suction caissons', EXPERT SYSTEMS WITH APPLICATIONS, vol. 38, no. 10, pp. 12608-12618.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH, Modaresnezhad, M & Mousavi, M 2011, 'New Ground-Motion Prediction Equations Using Multi Expression Programing', JOURNAL OF EARTHQUAKE ENGINEERING, vol. 15, no. 4, pp. 511-536.View/Download from: Publisher's site
Aminian, P, Javid, MR, Asghari, A, Gandomi, AH & Esmaeili, MA 2011, 'A robust predictive model for base shear of steel frame structures using a hybrid genetic programming and simulated annealing method', NEURAL COMPUTING & APPLICATIONS, vol. 20, no. 8, pp. 1321-1332.View/Download from: Publisher's site
Divsalar, M, Javid, MR, Gandomi, AH, Soofi, JB & Mahmood, MV 2011, 'HYBRID GENETIC PROGRAMMING-BASED SEARCH ALGORITHMS FOR ENTERPRISE BANKRUPTCY PREDICTION', APPLIED ARTIFICIAL INTELLIGENCE, vol. 25, no. 8, pp. 669-692.View/Download from: Publisher's site
Gandomi, AH & Alavi, AH 2011, 'Applications of computational intelligence in behavior simulation of concrete materials', Studies in Computational Intelligence, vol. 359, pp. 221-243.View/Download from: Publisher's site
The application of Computational Intelligence (CI) to structural engineering design problems is relatively new. This chapter presents the use of the CI techniques, and specifically Genetic Programming (GP) and Artificial Neural Network (ANN) techniques, in behavior modeling of concrete materials. We first introduce two main branches of GP, namely Tree-based Genetic Programming (TGP) and Linear Genetic Programming (LGP), and two variants of ANNs, called Multi Layer Perceptron (MLP) and Radial Basis Function (RBF). The simulation capabilities of these techniques are further demonstrated by applying them to two conventional concrete material cases. The first case is simulation of concrete compressive strength using mix properties and the second problem is prediction of elastic modulus of concrete using its compressive strength. © 2011 Springer-Verlag Berlin Heidelberg.
Gandomi, AH & Alavi, AH 2011, 'Multi-stage genetic programming: A new strategy to nonlinear system modeling', INFORMATION SCIENCES, vol. 181, no. 23, pp. 5227-5239.View/Download from: Publisher's site
Structural optimization is an important area related to both optimization and structural engineering. Structural optimization problems are often used as benchmarks to validate new optimization algorithms or to test the suitability of a chosen algorithm. In almost all structural engineering applications, it is very important to find the best possible parameters for given design objectives and constraints which are highly non-linear, involving many different design variables. The field of structural optimization is also an area undergoing rapid changes in terms of methodology and design tools. Thus, it is highly necessary to summarize some benchmark problems for structural optimization. This chapter provides an overview of structural optimization problems of both truss and non-truss cases. © 2011 Springer-Verlag Berlin Heidelberg.
Gandomi, AH, Alavi, AH & Yun, GJ 2011, 'Formulation of uplift capacity of suction caissons using multi expression programming', KSCE JOURNAL OF CIVIL ENGINEERING, vol. 15, no. 2, pp. 363-373.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH & Yun, GJ 2011, 'Nonlinear modeling of shear strength of SFRC beams using linear genetic programming', STRUCTURAL ENGINEERING AND MECHANICS, vol. 38, no. 1, pp. 1-25.
Gandomi, AH, Alavi, AH, Mirzahosseini, MR & Nejad, FM 2011, 'Nonlinear Genetic-Based Models for Prediction of Flow Number of Asphalt Mixtures', JOURNAL OF MATERIALS IN CIVIL ENGINEERING, vol. 23, no. 3, pp. 248-263.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH, Mousavi, M & Tabatabaei, SM 2011, 'A hybrid computational approach to derive new ground-motion prediction equations', ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, vol. 24, no. 4, pp. 717-732.View/Download from: Publisher's site
Gandomi, AH, Sahab, MG & Rahai, A 2011, 'A dynamic nondestructive damage detection methodology for orthotropic plate structures', STRUCTURAL ENGINEERING AND MECHANICS, vol. 39, no. 2, pp. 223-239.
Gandomi, AH, Tabatabaei, SM, Moradian, MH, Radfar, A & Alavi, AH 2011, 'A new prediction model for the load capacity of castellated steel beams', JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH, vol. 67, no. 7, pp. 1096-1105.View/Download from: Publisher's site
Gandomi, AH, Yang, X-S & Alavi, AH 2011, 'Mixed variable structural optimization using Firefly Algorithm', COMPUTERS & STRUCTURES, vol. 89, no. 23-24, pp. 2325-2336.View/Download from: Publisher's site
Hosseini, SSS, Jafarnejad, A, Behrooz, AH & Gandomi, AH 2011, 'Combined heat and power economic dispatch by mesh adaptive direct search algorithm', EXPERT SYSTEMS WITH APPLICATIONS, vol. 38, no. 6, pp. 6556-6564.View/Download from: Publisher's site
Mirzahosseini, MR, Aghaeifar, A, Alavi, AH, Gandomi, AH & Seyednour, R 2011, 'Permanent deformation analysis of asphalt mixtures using soft computing techniques', EXPERT SYSTEMS WITH APPLICATIONS, vol. 38, no. 5, pp. 6081-6100.View/Download from: Publisher's site
Mollahasani, A, Alavi, AH & Gandomi, AH 2011, 'Empirical modeling of plate load test moduli of soil via gene expression programming', COMPUTERS AND GEOTECHNICS, vol. 38, no. 2, pp. 281-286.View/Download from: Publisher's site
Mollahasani, A, Alavi, AH, Gandomi, AH & Rashed, A 2011, 'Nonlinear neural-based modeling of soil cohesion intercept', KSCE JOURNAL OF CIVIL ENGINEERING, vol. 15, no. 5, pp. 831-840.View/Download from: Publisher's site
Mousavi, SM, Alavi, AH, Gandomi, AH & Mollahasani, A 2011, 'Nonlinear genetic-based simulation of soil shear strength parameters', JOURNAL OF EARTH SYSTEM SCIENCE, vol. 120, no. 6, pp. 1001-1022.View/Download from: Publisher's site
Mousavi, SM, Alavi, AH, Mollahasani, A & Gandomi, AH 2011, 'A hybrid computational approach to formulate soil deformation moduli obtained from PLT', ENGINEERING GEOLOGY, vol. 123, no. 4, pp. 324-332.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH & Gandomi, M 2010, 'Comment on 'Sivapragasam C, Maheswaran R, Venkatesh V. 2008. Genetic programming approach for flood routing in natural channels. Hydrological Processes 22: 623-628'', HYDROLOGICAL PROCESSES, vol. 24, no. 6, pp. 798-799.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH & Heshmati, AAR 2010, 'Discussion on "Soft computing approach for real-time estimation of missing wave heights" by SN Londhe [Ocean Engineering 35 (2008) 1080-1089]', OCEAN ENGINEERING, vol. 37, no. 13, pp. 1239-1240.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH, Mollahassani, A, Heshmati, AA & Rashed, A 2010, 'Modeling of maximum dry density and optimum moisture content of stabilized soil using artificial neural networks', JOURNAL OF PLANT NUTRITION AND SOIL SCIENCE, vol. 173, no. 3, pp. 368-379.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH, Mousavi, M & Mollahasani, A 2010, 'High-precision modeling of uplift capacity of suction caissons using a hybrid computational method', GEOMECHANICS AND ENGINEERING, vol. 2, no. 4, pp. 253-280.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH, Sahab, MG & Gandomi, M 2010, 'Multi expression programming: a new approach to formulation of soil classification', ENGINEERING WITH COMPUTERS, vol. 26, no. 2, pp. 111-118.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH & Sahab, MG 2010, 'New formulation for compressive strength of CFRP confined concrete cylinders using linear genetic programming', MATERIALS AND STRUCTURES, vol. 43, no. 7, pp. 963-983.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH & Taghipour, A 2010, 'Discussion on "Alternative data-driven methods to estimate wind from waves by inverse modeling" by Mansi Daga, M. C. Deo [Natural Hazards (2008) NHAZ 524, Article 9299, DOI 10.1007/s11069-008-9299-2]', NATURAL HAZARDS, vol. 52, no. 3, pp. 671-673.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH, Arjmandi, P, Aghaeifar, A & Seyednour, R 2010, 'GENETIC PROGRAMMING AND ORTHOGONAL LEAST SQUARES: A HYBRID APPROACH TO MODELING THE COMPRESSIVE STRENGTH OF CFRP-CONFINED CONCRETE CYLINDERS', JOURNAL OF MECHANICS OF MATERIALS AND STRUCTURES, vol. 5, no. 5, pp. 735-753.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH, Sahab, MG & Arjmandi, P 2010, 'Formulation of elastic modulus of concrete using linear genetic programming', JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY, vol. 24, no. 6, pp. 1273-1278.View/Download from: Publisher's site
Hosseini, SSS & Gandomi, AH 2010, 'Discussion of "Economic Load Dispatch-A Comparative Study on Heuristic Optimization Techniques With an Improved Coordinated Aggregation-Based PSO"', IEEE TRANSACTIONS ON POWER SYSTEMS, vol. 25, no. 1, pp. 590-590.View/Download from: Publisher's site
Hosseini, SSS & Gandomi, AH 2010, 'Discussion on "Enhancement of combined heat and power economic dispatch using self adaptive real-coded genetic algorithm, by P. Subbaraj et al., Applied Energy 86 (2009) 915-921."', APPLIED ENERGY, vol. 87, no. 4, pp. 1459-1459.View/Download from: Publisher's site
Mousavi, SM, Alavi, AH, Gandomi, AH, Esmaeili, MA & Gandomi, M 2010, 'A data mining approach to compressive strength of CFRP-confined concrete cylinders', STRUCTURAL ENGINEERING AND MECHANICS, vol. 36, no. 6, pp. 759-783.View/Download from: Publisher's site
Mousavi, SM, Gandomi, AH, Alavi, AH & Vesalimahmood, M 2010, 'Modeling of compressive strength of HPC mixes using a combined algorithm of genetic programming and orthogonal least squares', STRUCTURAL ENGINEERING AND MECHANICS, vol. 36, no. 2, pp. 225-241.View/Download from: Publisher's site
Sadat Hosseini, SS & Gandomi, AH 2010, 'WITHDRAWN: Discussion of “Combined heat and power economic dispatch by harmony search algorithm” by A. Vasebi et al., International Journal of Electrical Power and Energy Systems 29 (2007) 713–719', International Journal of Electrical Power & Energy Systems.View/Download from: Publisher's site
Singh, AK, Deo, MC & Kumar, VS 2010, 'Discussion: Neural network - genetic programming for sediment transport', PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-MARITIME ENGINEERING, vol. 163, no. 3, pp. 135-136.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH, Gandomi, M & Sadat Hosseini, SS 2009, 'Prediction of maximum dry density and optimum moisture content of stabilised soil using RBF neural networks', IES Journal Part A: Civil and Structural Engineering, vol. 2, no. 2, pp. 98-106.View/Download from: Publisher's site
This article proposes a novel approach for the prediction of maximum dry density (MDD) and optimum moisture content (OMC) of soil-stabiliser mix by using radial basis function (RBF) neural networks. RBF neural network is utilised to construct comprehensive and accurate models to be able to relate the MDD and OMC of stabilised soil to the properties of natural soil such as particle size distribution, plasticity, linear shrinkage and the type and quantity of stabilising additives. Two separate sets of RBF prediction models, one for the MDD and the other for the OMC, have been developed. A parametric study was also conducted in this study using the results obtained from the proposed models to evaluate the sensitivity of MDD and OMC due to variation of the influencing parameters. A comprehensive set of data including a wide range of soil types obtained from previously published stabilisation test results was used for training, validation and testing the prediction models. The accuracy of the proposed models is satisfactory when compared with that of the experimental results. The results of proposed RBF models were further compared with those of the existing models found in literature and found to be more accurate. © 2009 Taylor & Francis Group, LLC.
Gandomi, AH & Alavi, AH 2009, 'Discussion on "Predicting the shear strength of reinforced concrete beams using artificial neural networks" by MY Mansour, M. Dicleli, JY Lee, J. Zhang [Eng Struct 26 (2004) 781-799]', ENGINEERING STRUCTURES, vol. 31, no. 11, pp. 2801-2801.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH, Kazemi, S & Alinia, MM 2009, 'Behavior appraisal of steel semi-rigid joints using Linear Genetic Programming', JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH, vol. 65, no. 8-9, pp. 1738-1750.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH & Hosseini, SSS 2008, 'A Discussion on "Genetic programming for retrieving missing information in wave records along the west coast of India" [Applied Ocean Research 2007; 29 (3): 99-111]', APPLIED OCEAN RESEARCH, vol. 30, no. 4, pp. 338-339.View/Download from: Publisher's site
Mohebali, B, Tahmassebi, A, Meyer-Baese, A & Gandomi, AH 2020, 'Probabilistic neural networks: A brief overview of theory, implementation, and application' in Handbook of Probabilistic Models, Elsevier, The Netherlands, pp. 347-367.View/Download from: Publisher's site
© 2020 Elsevier Inc. All rights reserved. Probabilistic neural networks (PNNs) offer a scalable alternative to the conventional back-propagation neural networks in classification problems without the need for massive forward and backward calculations that is associated with the ordinary neural networks. In addition, they can work with smaller sets of training data. However, this advantage may come at a cost of requiring large amounts of memory as the training data get larger. This chapter takes a look at the fundamental mathematics behind the modern PNNs, their application, and approaches that address some practical issues that come with them.
Ramezani, F, Naderpour, M, Taheri, J, Romanous, J & Zomaya, AY 2020, 'Task Scheduling in Cloud Environments: A Survey on Population-based Evolutionary Algorithms' in Gandomi, AH, Emrouznejad, A, Jamshidi, MM, Deb, K & Rahimi, I (eds), Evolutionary Computation in Scheduling, John Wiley & Sons.
This book: Provides a representative sampling of real-world problems currently being tackled by practitioners Examines a variety of single-, multi-, and many-objective problems that have been solved using evolutionary computations, ...
Dutta, S & Gandomi, AH 2019, 'Design of experiments for uncertainty quantification based on polynomial chaos expansion metamodels' in Handbook of Probabilistic Models, pp. 369-381.View/Download from: Publisher's site
© 2020 Elsevier Inc. All rights reserved. In the past decade, uncertainty quantification (UQ) has received much attention, particularly in the research areas of reliability and risk analysis, sensitivity analysis, and optimization under uncertainty, to mention a few. In the context of UQ, one of the major challenges is the computational demand of the numerical (finite element) model that is used to analyze the large-scale engineering systems under consideration. Metamodels or surrogate models are often used as substitutes to those high-fidelity numerical models to overcome this issue. Polynomial chaos expansion (PCE) has been considered as one of the promising metamodeling methods. To build a PCE metamodel, design of experiments (DoEs) are carried out, i.e., determining the design points (in the input space) where the original (high-fidelity) computational model needs to be evaluated. The accuracy level of the metamodel depends on the DoE over the input design space. This chapter will introduce some state-of-the-art DoEs used for uncertainty quantification problems. A comparative study is performed to show the efficiency and limitations of the various experimental designs in uncertainty quantification of engineered systems with varying input dimensionality and computational complexity.
Jayabarathi, T, Raghunathan, T & Gandomi, AH 2018, 'The bat algorithm, variants and some practical engineering applications: A review' in Studies in Computational Intelligence, pp. 313-330.View/Download from: Publisher's site
© Springer International Publishing AG 2018. The bat algorithm (BA), a metaheuristic algorithm developed by Xin-She Yang in 2010, has since been modified, and applied to numerous practical optimization problems in engineering. This chapter is a survey of the BA, its variants, some sample real-world optimization applications, and directions for future research.
Tahmassebi, A & Gandomi, AH 2018, 'Genetic Programming Based on Error Decomposition: A Big Data Approach' in Genetic Programming Theory and Practice XV, Springer International Publishing, pp. 135-147.View/Download from: Publisher's site
© 2017 Elsevier Inc. All rights reserved. The literature shows that the Gravitational Search Algorithm (GSA) is really competitive compared to Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) on benchmark functions. However, local optima entrapment and slow convergence are hindrances when solving real engineering problems. Such issues originate from slow movement of masses due to nearly equal weight proportional to the number of iterations. In this study, 10 chaotic maps tune the gravitational constant (. G) to overcome these problems. The gravitational constant balances exploration and exploitation, so chaotic maps are allowed to perform this duty in this study. Ten unconstrained benchmark functions examine the proposed Chaotic GSA (CGSA) algorithms. This work also considers finding the optimal design for welded beam and pressure vessel designs to prove the applicability of the proposed method. The results prove that chaotic maps improve the performance of GSA.
Sahab, MG, Toropov, VV & Gandomi, AH 2017, 'Optimum Design of Composite Concrete Floors Using a Hybrid Genetic Algorithm' in Handbook of Neural Computation, pp. 581-589.View/Download from: Publisher's site
© 2017 Elsevier Inc. All rights reserved. Composite steel-concrete floors are used commonly in bridge decks and as story floors in different kind of buildings. This paper presents optimal design of composite floors consisting of steel joists and a concrete slab on the top of them. The slab thickness, typical joist spacing, and the standard steel section size of joists are considered as design variables. The objective function is the cost of unit area of composite floor including cost of materials and labors for reinforcement, concrete, formwork and steel joists. AISC-ASD89 and EURO-4 (EN 1994) code provisions are considered to define design constraints. A hybrid algorithm based on a modified genetic algorithm is implemented to solve the optimization problem. The effect of many design parameters such as unit cost of materials and characteristic strength of concrete on the optimum solution is investigated using a practical design example.
Alavi, AH, Gandomi, AH, Mollahasani, A & Bazaz, JB 2013, 'Linear and Tree-Based Genetic Programming for Solving Geotechnical Engineering Problems' in Metaheuristics in Water, Geotechnical and Transport Engineering, pp. 289-310.View/Download from: Publisher's site
Gandomi, AH & Alavi, AH 2013, 'Expression Programming Techniques for Formulation of Structural Engineering Systems' in Metaheuristic Applications in Structures and Infrastructures, pp. 439-455.View/Download from: Publisher's site
A new metaheuristic optimization algorithm, called krill herd (KH), has been recently proposed by Gandomi and Alavi. In this study, KH is introduced for structural optimization. For more verification, KH is subsequently applied to three design problems reported in the literature. The performance of the KH algorithm is further compared with various algorithms representative of the state of the art in the area. The comparisons show that the results obtained by KH can be better than the best solutions obtained by the existing methods in these three case studies. © 2013 Copyright © 2013 Elsevier Inc. All rights reserved.
Gandomi, AH, Yang, XS, Talatahari, S & Alavi, AH 2013, 'Metaheuristic Algorithms in Modeling and Optimization' in Metaheuristic Applications in Structures and Infrastructures, pp. 1-24.View/Download from: Publisher's site
Sahab, MG, Toropov, VV & Gandomi, AH 2013, 'A Review on Traditional and Modern Structural Optimization: Problems and Techniques' in Metaheuristic Applications in Structures and Infrastructures, pp. 25-47.View/Download from: Publisher's site
Alavi, AH, Gandomi, AH & Mollahasani, A 2012, 'A genetic programming-based approach for the performance characteristics assessment of stabilized soil' in Variants of Evolutionary Algorithms for Real-World Applications, pp. 343-376.View/Download from: Publisher's site
© 2012 Springer-Verlag Berlin Heidelberg. All rights are reserved. This chapter presents a variant of genetic programming, namely linear genetic programming (LGP), and a hybrid search algorithm coupling LGP and simulated annealing (SA), called LGP/SA, to predict the performance characteristics of stabilized soil. LGP and LGP/SA relate the unconfined compressive strength (UCS), maximum dry density (MDD), and optimum moisture content (OMC) metrics of stabilized soil to the properties of the natural soil as well as the types and quantities of stabilizing additives. Different sets of LGP and LGP/SA-based prediction models have been separately developed. The contributions of the parameters affecting UCS, MDD, and OMC are evaluated through a sensitivity analysis. A subsequent parametric analysis is carried out and the trends of the results are compared with previous studies. A comprehensive set of data obtained from the literature has been used for developing the models. Experimental results confirm that the accuracy of the proposed models is satisfactory. In particular, the LGP-based models are found to be more accurate than the LGP/SA-based models.
Mohebali, B, Tahmassebi, A, Gandomi, AH & Meyer-Baese, A 2019, 'A big data inspired preprocessing scheme for bandwidth use optimization in smart cities applications', BIG DATA: LEARNING, ANALYTICS, AND APPLICATIONS, Conference on Big Data - Learning, Analytics, and Applications, SPIE-INT SOC OPTICAL ENGINEERING, Baltimore, MD.View/Download from: Publisher's site
Tahmassebi, A, Ehtemami, A, Mohebali, B, Gandomi, AH, Pinker, K & Meyer-Base, A 2019, 'Big Data Analytics in Medical Imaging using Deep Learning', BIG DATA: LEARNING, ANALYTICS, AND APPLICATIONS, Conference on Big Data - Learning, Analytics, and Applications, SPIE-INT SOC OPTICAL ENGINEERING, Baltimore, MD.View/Download from: Publisher's site
Mohebali, B, Tahmassebi, A, Gandomi, AH, Meyer-Baese, A & Foo, SY 2018, 'A Scalable Communication Abstraction Framework for Internet of Things Applications using Raspberry Pi', DISRUPTIVE TECHNOLOGIES IN INFORMATION SCIENCES, Conference on Disruptive Technologies in Information Sciences, SPIE-INT SOC OPTICAL ENGINEERING, Orlando, FL.View/Download from: Publisher's site
Tahmassebi, A, Gandomi, AH & Meyer-Baese, A 2018, 'A Pareto Front Based Evolutionary Model for Airfoil Self-Noise Prediction', 2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI), IEEE, Rio de Janeiro, BRAZIL, pp. 909-916.View/Download from: Publisher's site
Tahmassebi, A, Gandomi, AH & Meyer-Baese, A 2018, 'An Evolutionary Online Framework for MOOC Performance using EEG Data', 2018 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI), IEEE, Rio de Janeiro, BRAZIL, pp. 895-902.View/Download from: Publisher's site
Tahmassebi, A, Schulte, MHJ, Gandomi, AH, Goudriaan, AE, McCann, I & Meyer-Baese, A 2018, 'Deep learning in medical imaging: FMRI big data analysis via convolutional neural networks', ACM International Conference Proceeding Series.View/Download from: Publisher's site
© 2018 Copyright held by the owner/author(s). This paper aims at implementing novel biomarkers extracted from functional magnetic resonance imaging (fMRI) images taken at resting-state using convolutional neural networks (CNN) to predict relapse in heavy smoker subjects. In this regard, two classes of subjects were studied. The first class contains 19 subjects that took the drug N-acetylcysteine (NAC), and the second class contains 20 subjects that took a placebo. The subjects underwent a double-blind smoking cessation treatment. The resting-state fMRI of the subjects' brains were recorded through 200 snapshots before and after the treatment. The relapse data was assessed after 6 months past the treatment. The data was pre-processed and an undercomplete autoencoder along with various similarity metrics was developed to extract salient features that could differentiate the pre and post treatment images. Finally, the extracted feature matrix was fed into robust classification algorithms to classify the subjects in terms of relapse and non-relapse. The XGBoost algorithm with 0.86 precision and an AUC of 0.92 outperformed the other classification methods in prediction of relapse in subjects.
Tahmassebi, A, Gandomi, AH & Meyer-Bäse, A 2017, 'High performance GP-Based approach for fMRI big data classification', ACM International Conference Proceeding Series.View/Download from: Publisher's site
© 2017 Copyright is held by the owner/author(s). We consider resting-state Functional Magnetic Resonance Imaging (fMRI) of two classes of patients: one that took the drug Nacetylcysteine (NAC) and the other one a placebo before and after a smoking cessation treatment. Our goal was to classify the relapse in nicotine-dependent patients as treatment or non-Treatment based on their fMRI scans. 80% accuracy was obtained using Independent Component Analysis (ICA) along with Genetic Programming (GP) classifier using High Performance Computing (HPC) which we consider significant enough to suggest that there is a difference in the resting-state fMRI images of a smoker that undergoes this smoking cessation treatment compared to a smoker that receives a placebo.
Tahmassebi, A, Gandomi, AH, McCann, I, Schulte, MHJ, Schmaal, L, Goudriaan, AE & Meyer-Baese, A 2017, 'An Evolutionary Approach for fMRI Big Data Classification', 2017 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), IEEE Congress on Evolutionary Computation (CEC), IEEE, SPAIN, pp. 1029-1036.
Trivedi, IN, Gandomi, AH, Jangir, P & Jangir, N 2016, 'Study of Different Boundary Constraint Handling Schemes in Interior Search Algorithm', ARTIFICIAL INTELLIGENCE AND EVOLUTIONARY COMPUTATIONS IN ENGINEERING SYSTEMS, ICAIECES 2016, International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems (ICAIECES), SPRINGER-VERLAG BERLIN, SRM Univ, Chennai, INDIA, pp. 547-564.View/Download from: Publisher's site
Trivedi, IN, Gandomi, AH, Jangir, P, Kumar, A, Jangir, N & Totlani, R 2017, 'Adaptive krill herd algorithm for global numerical optimization', Advances in Intelligent Systems and Computing, pp. 517-525.View/Download from: Publisher's site
© Springer Nature Singapore Pte Ltd. 2017. A recent bio-inspired optimization algorithm, that is, based on the Lagrangian and evolutionary behavior of krill individuals in nature is called the Krill Herd (KH) Algorithm. Randomization has a key role in both exploration and exploitation of a problem using KH algorithm. A new randomization technique termed adaptive technique is integrated with Krill Herd algorithm and tested on several global numerical functions. The KH uses Lagrangian movement which includes induced movement, random diffusion, and foraging motion, and therefore, it covers a vast area in the exploration phase. And then adding the powerful adaptive randomization technique potent the adaptive KH (AKH) algorithm to attain global optimal solution with faster convergence as well as less parameter dependency. The proposed AKH outperforms the standard KH in terms of both statistical results and best solution.
Gandomi, AH & Kashani, AR 2016, 'Evolutionary Bound Constraint Handling for Particle Swarm Optimization', 2016 4TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL AND BUSINESS INTELLIGENCE (ISCBI), 4th International Symposium on Computational and Business Intelligence (ISCBI), IEEE, Olten, SWITZERLAND, pp. 148-152.
Kanagaraj, G, Ponnambalam, SG & Gandomi, AH 2015, 'Hybridizing Cuckoo Search with Bio-inspired Algorithms for Constrained Optimization Problems', SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING (SEMCCO 2015), 6th International Conference on Swarm, Evolutionary and Memetic Computing (SEMCCO), SPRINGER INTERNATIONAL PUBLISHING AG, CMR Tech Campus, Hyderabad, INDIA, pp. 260-273.View/Download from: Publisher's site
Gandomi, AH, Kashani, AR & Mousavi, M 2015, 'Boundary constraint handling affection on slope stability analysis', Computational Methods in Applied Sciences, pp. 341-358.View/Download from: Publisher's site
© 2015 Springer International Publishing Switzerland. In an engineering optimization problem such as soil slope problem, each design variable has permissible solution domain. Therefore, efficiency of an optimization algorithm may be affected by the method used for keeping the solutions within the defined boundaries or boundary constraint handling method. Despite importance of selecting constraint handling approach, there aren't adequate studies in this field. Heterogeneous slope stability optimization in the presence of a band of weak soil layer is considered as a complex geotechnical problem that requires satisfying boundary constraints. Evolutionary boundary constraint handling is one of the recently proposed methods that is very easy to implement and very effective. The present study intended to improve the optimization results by means of evolutionary boundary constraint handling scheme on slope stability optimization problem. In the current chapter five benchmark problems are analyzed using absorbing and evolutionary boundary constraint handling schemes and their results are compared to check the validity of this method. Based on achieved results optimization algorithm performance is improved by using the proposed boundary constraint handling method.
Hosseini, SSS, Gandomi, AH, Nemati, A & Hosseini, SHS 2015, 'Reactive power and voltage control based on mesh adaptive direct search algorithm', Computational Methods in Applied Sciences, pp. 217-231.View/Download from: Publisher's site
© 2015 Springer International Publishing Switzerland. This is a pioneer study that presents a new optimization algorithm called mesh adaptive direct search (MADS) to solve optimal steady-state performance of power systems. MADS is utilized to specify the optimal settings of control variables, i.e. transformer taps and generator voltages for optimal reactive power and voltage control of IEEE 30-bus system. Covariance matrix adaptation evolution strategy (CMAES) algorithm is utilized as a strong search strategy in the MADS technique to enhance its effectiveness. The results acquired by the hybrid search algorithm coupling MADS and CMAES, called MADS-CMAES, and the MADS algorithm itself without any search method are compared with multi-objective evolutionary and particle swarm optimization algorithms, demonstrating the superiority of MADS. The proposed MADS-based techniques are very robust against their parameters and changing the search space because of their inherent adaptive tuning.
Shahrara, N, Çelik, T & Gandomi, AH 2015, 'Analysing build-operate-transfer models in utility projects', Civil-Comp Proceedings.
© Civil-Comp Press, 2015. In this paper the financial viability of undertaking build-operate-transfer (BOT) contracts for sewer and water projects in California, United States of America is analysed. Furthermore with the aid of sensitivity analysis, risk parameters are identified. Sensitivity analysis results demonstrate that project construction cost determines the financial viability of undertaking a BOT contract; therefore, reliable construction cost prediction, based on limited information, at the early stages of the project planning phase is crucial for development of an objective BOT agreement. This study utilizes gene expression programming (GEP), an extension of the genetic algorithm and genetic programming, to develop a prediction model for estimating the construction cost of water and sewer rehabilitation/replacement projects. A database used for developing the model was established on the basis of data related to 210 water and sewer projects obtained from the City of San Diego, California, United States of America.
Wang, G-G, Deb, S, Gandomi, AH & Alavi, AH 2015, 'A Hybrid PBIL-Based Krill Herd Algorithm', 2015 3RD INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL AND BUSINESS INTELLIGENCE (ISCBI 2015), 3rd International Symposium on Computational and Business Intelligence (ISCBI), IEEE, Bali, INDONESIA, pp. 39-44.View/Download from: Publisher's site
Gandomi, AH & Roke, DA 2014, 'Engineering Optimization using Interior Search Algorithm', 2014 IEEE SYMPOSIUM ON SWARM INTELLIGENCE (SIS), IEEE Symposium on Swarm Intelligence (SIS), IEEE, Orlando, FL, pp. 20-26.
Gandomi, AH & Roke, DA 2014, 'Seismic response prediction of self-centering, concentrically-braced frames using genetic programming', Structures Congress 2014 - Proceedings of the 2014 Structures Congress, pp. 1221-1232.View/Download from: Publisher's site
© 2014 American Society of Civil Engineers. Conventional concentrically braced frame (CBF) systems are commonly used in earthquake-resistant structural systems. However, they have limited drift capacity before brace buckling occurs. Self-centering, concentrically-braced frame (SC-CBF) systems have recently been developed to increase drift capacity prior to initiation of damage and to minimize residual drift. SC-CBFs have more complex behavior than conventional CBFs. The seismic response of SC-CBFs depends on many new parameters such as rocking behavior, post-tensioning bars, and energy dissipation elements. Additionally, uncertainty of mechanical properties (e.g., coefficient of friction in the friction-bearings) can affect the system response. To design SC-CBF systems, an accurate prediction of the statistical parameters of roof drift demand is essential. In this study, genetic programming is used to predict the mean and standard deviation of SC-CBF peak roof drift response under the design basis earthquake using the most effective mechanical and geometric parameters. The results of this study can then be used in the future to design more efficient SC-CBF systems with a more accurate roof drift prediction.
Wang, G-G, Deb, S, Gandomi, AH, Zhang, Z & Alavi, AH 2014, 'A Novel Cuckoo Search with Chaos Theory and Elitism Scheme', 2014 INTERNATIONAL CONFERENCE ON SOFT COMPUTING & MACHINE INTELLIGENCE ISCMI 2014, 2014 International Conference on Soft Computing & Machine Intelligence (ISCMI 2014), IEEE, New Delhi, INDIA, pp. 64-69.View/Download from: Publisher's site
Gandomi, AH, Alavi, AH, Ting, TO & Yang, XS 2013, 'Intelligent modeling and prediction of elastic modulus of concrete strength via gene expression programming', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 564-571.View/Download from: Publisher's site
The accurate prediction of the elastic modulus of concrete can be very important in civil engineering applications. We use gene expression programming (GEP) to model and predict the elastic modulus of normal-strength concrete (NSC) and high-strength concrete (HSC). The proposed models can relate the modulus of elasticity of NSC and HSC to their compressive strength, based on reliable experimental databases obtained from the published literature. Our results show that GEP can be an effective method for deriving simplified and precise formulations for the elastic modulus of NSC and HSC. Furthermore, the comparison study in the present work indicates that the GEP predictions are more accurate than other methods. © 2013 Springer-Verlag Berlin Heidelberg.
Heshmati, RAA, Alavi, AH, Keramati, M & Gandomi, AH 2009, 'A radial basis function neural network approach for compressive strength prediction of stabilized soil', Geotechnical Special Publication, pp. 147-153.View/Download from: Publisher's site
This study considers the use of artificial neural networks (ANNs) to predict the unconfined compressive strength (UCS) of soil-stabilizer mix. Radial basis function (RBF) as one of the most widely used ANN architectures is utilized to construct comprehensive models to relate the UCS of stabilized soil to the properties of natural soil and type and quantity of stabilizing additives. A comprehensive set of data obtained from previously published stabilization test results was used for model development. A subsequent parametric study was carried out and the trends of the results have been confirmed via previous laboratory studies. The RBF based estimates are compared with the experimental and numerical results of other researchers and found to be more accurate. © 2009 ASCE.
Alavi, AH, Heshmati, AA, Gandomi, AH, Askarinejad, A & Mirjalili, M 2008, 'Utilisation of computational intelligence techniques for stabilised soil', Proceedings of the 6th International Conference on Engineering Computational Technology.
One of the main objectives of chemical stabilisation is to increase the compressive strength of soils. A wide range of parameters affect the strength improvement in cementitious stabilisation with chemicals. Accordingly, it is difficult to determine some kinds of functional relationships in strength improvement which make the precision of strength prediction to be satisfying. The purpose of the present study is to use two computational intelligence techniques namely, multilayer perceptron (MLP) and linear genetic programming (LGP), in order to develop the mathematical models to be capable of predicting the unconfined compressive strength. Subsequently, a comparison between these methods was performed in terms of prediction performance. Properties of natural soil such as textural properties, plasticity and linear shrinkage, stabiliser quantities and types (cement, lime, asphalt), for a wide range of soil types were used in order to generate the mathematical models to be able to predict the compressive strength as a quality of stabilised soil. A comprehensive set of data including 219 previously published stabilised unconfined compressive strength experimental determinations were utilised to develop the models. © 2008 Civil-Comp Press.
Alavi, AH, Heshmati, AA, Salehzadeh, H, Gandomi, AH & Askarinejad, A 2008, 'Soft computing based approaches for high performance concrete', Proceedings of the 6th International Conference on Engineering Computational Technology.
Different parameters influence the compressive strength and workability properties of high performance concrete (HPC) mixes. Accordingly, an extensive understanding of relation between these parameters and properties of the resulting matrix is required for developing a standard mix design procedure for HPC mix. The complex behaviour of strength and workability improvement and a need to avoid trying several mix proportions to generate a successful mix suggest the necessity to develop comprehensive mathematical models to be able to evaluate the performance characteristics of HPC mixes with high accuracy. Therefore, in this paper, linear genetic programming (LGP) is utilised for the first time in the literature, to develop mathematical models to be able to predict the strength and slump flow of HPC mixes from the influencing parameters. Subsequently, the LGP based prediction results are compared with the results of proposed multilayer perceptron (MLP) in terms of prediction performance. Sand cement ratio, coarse aggregate cement ratio, water cement ratio, percentage of silica fume and percentage of superplasticiser are used as the input variables to the models to predict the strength and slump flow of HPC mixes. A reliable database was obtained from the previously published literature in order to develop the models. © 2008 Civil-Comp Press.
Gandomi, AH, Alavi, AH, Sahab, MG, Gandomi, M & Gorji, MS 2008, 'Empirical models for prediction of flexural resistance and initial stiffness of welded beam-column joints', EASEC-11 - Eleventh East Asia-Pacific Conference on Structural Engineering and Construction.
Gandomi, AH, Alavi, AH, Sahab, MG, Gandomi, M & Safari Gorji, M 2008, 'Empirical models for the prediction of flexural resistance and initial stiffness of welded beam-column joints', EASEC-11 - Eleventh East Asia-Pacific Conference on Structural Engineering and Construction.
Welded beam-column joints play a fundamental role in the global response of steel structures. The flexural resistance and initial stiffness properties of the joints are affected by different parameters. It is idealistic to develop models, relating these properties of the joints to the influencing parameters. This paper proposes a novel approach for the prediction of flexural resistance and initial stiffness of welded joints by using a hybrid search algorithm that couples genetic programming (GP) and simulated annealing (SA), called GP/SA. Column height, column flange width, column flange thickness, column flange yield stress, column web thickness, column web yield stress, beam height, beam web thickness, beam web yield stress, beam flange thickness, beam flange width, beam flange yield stress, and weld thickness are used as input variables to the models. A reliable database from the previously published literature was employed to develop the empirical models. The accuracy of the proposed models is satisfactory as compared to experimental results. GP/SA models are further compared with the corresponding design code (Eurocode 3) reference values. The results demonstrate that the GP/SA based models have better performance than Eurocode 3 models.
Gorji, MS, Taribakhsh, M, Gandomi, A & Makhsous, SJ 2008, 'Seismic assessment and retrofit of khorjinee frames in typical Iranian steel buildings', EASEC-11 - Eleventh East Asia-Pacific Conference on Structural Engineering and Construction.
Previous post-earthquake inspections and evaluation of damage in buildings in Iran have demonstrated that many steel frame buildings were suffered from high rate of damage due to lack of shear and moment capacities in the connections, especially those with common Khorjinee connections. Khorjinee connection is one of the beam-to-column connections in steel frame structures. It has become especially popular in Iran for conventional buildings, due to its speed and convenient in construction process and economic privileges in comparison to the other types of steel connections. The frames with Khorjinee connections are vulnerable to earthquakes and there are serious concerns about their performance in future earthquakes.