Particle Swarm Optimization: A Comprehensive Survey

Shami, Tareq M.; El-Saleh, Ayman A.; Alswaitti, Mohammed; Al-Tashi, Qasem; Summakieh, Mhd Amen; Mirjalili, Seyedali

doi:10.1109/access.2022.3142859

Cited by 1,433Metaheuristic Optimization Algorithms ResearchAntenna Design and OptimizationDistributed Control Multi-Agent Systems

Particle Swarm Optimization: A Comprehensive Survey

Open access

Shami, Tareq M., El-Saleh, Ayman A., Alswaitti, Mohammed, Al-Tashi, Qasem, Summakieh, Mhd Amen, Mirjalili, Seyedali·2022·IEEE Access

DOI 10.1109/access.2022.3142859

Open this paper in CoChat — free account

Abstract

Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

Integrity signals

Registry

Crossref · 10.1109/access.2022.3142859

Cites

281 of 281 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cited works that were retracted

Works this paper cites that were later retracted, from the retraction notices publishers deposit. Major when the notice came out before this paper was published.

Cites a retracted work: 10.1016/j.micpro.2020.103527
Major
The cited work was retracted on 2021-02-01 (notice 10.1016/j.micpro.2020.103527), before this paper was published (2022). Its authors could have known.
Cited work 10.1016/j.micpro.2020.103527 · retracted 2021-02-01 · notice 10.1016/j.micpro.2020.103527 · this paper published 2022

1 of 1

1 retracted

RETRACTED: A Fast 2-D Otsu lung tissue image segmentation algorithm based on improved PSO
Zhao, Y., Yu, X., Wu, H., et al. (2020) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2020.103527 · Cited by 29 · see citing works
Retracted
Particle swarm optimization
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192 · see citing works
Adaptation in Natural and Artificial Systems
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,340 · see citing works
Differential evolution- A simple and efficient heuristic for global optimization over continuous spaces
Storn, R., & Price, K. (1997) · Journal of Global Optimization · doi:10.1023/a:1008202821328 · Cited by 29,569 · see citing works
Grey Wolf Optimizer
Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2013.12.007 · Cited by 20,065 · see citing works
Self-citation
The Whale Optimization Algorithm
Mirjalili, S., & Lewis, A. (2016) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2016.01.008 · Cited by 14,829 · see citing works
Self-citation
A modified particle swarm optimizer
Shi, Y., & Eberhart, R. (1998) · 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360) · doi:10.1109/icec.1998.699146 · Cited by 11,497 · see citing works
The particle swarm - explosion, stability, and convergence in a multidimensional complex space
Clerc, M., & Kennedy, J. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.985692 · Cited by 9,053 · see citing works
An introduction to variable and feature selection
GuyonIsabelle, & ElisseeffAndré (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944968 · Cited by 7,843 · see citing works
An Introduction to Variable and Feature Selection.
Guyon, I., & Elisseeff, A. (2003) · Cited by 6,576 · see citing works
Ant colony optimization
Dorigo, M., Birattari, M., & Stützle, T. (2006) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2006.329691 · Cited by 5,338 · see citing works
SPEA2: Improving the strength pareto evolutionary algorithm
Zitzler, E., Laumanns, M., & Thiele, L. (2001) · doi:10.3929/ethz-a-004284029 · Cited by 5,069 · see citing works
A discrete binary version of the particle swarm algorithm
Kennedy, J., & Eberhart, R. C. (2002) · 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation · doi:10.1109/icsmc.1997.637339 · Cited by 4,767 · see citing works
Firefly Algorithms for Multimodal Optimization
Yang, X. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04944-6_14 · Cited by 4,103 · see citing works
Empirical study of particle swarm optimization
Shi, Y., & Eberhart, R. C. (2003) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.1999.785511 · Cited by 3,960 · see citing works
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
Liang, J., Qin, A. K., Suganthan, P. N., & Baskar, S. S. (2006) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2005.857610 · Cited by 3,755 · see citing works
Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces
Price, K. V. (2004) · Cited by 3,350 · see citing works
Particle swarm optimization algorithm: An overview
Wang, D., Tan, D., & Liu, L. (2018) · Soft Computing · doi:10.1007/s00500-016-2474-6 · Cited by 3,343 · see citing works
Self-Organizing Hierarchical Particle Swarm Optimizer With Time-Varying Acceleration Coefficients
Ratnaweera, A., Halgamuge, S. K., & Watson, H. C. (2004) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2004.826071 · Cited by 3,050 · see citing works
The Arithmetic Optimization Algorithm
Abualigah, L., Diabat, A. H., Mirjalili, S., Elaziz, M. A., & Gandomi, A. H. (2021) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2020.113609 · Cited by 2,954 · see citing works
Self-citation
Evolution strategies – A comprehensive introduction
Beyer, H.-G., & Schwefel, H. (2002) · Natural Computing · doi:10.1023/a:1015059928466 · Cited by 2,810 · see citing works
Marine Predators Algorithm: A nature-inspired metaheuristic
Faramarzi, A., Heidarinejad, M., Mirjalili, S., et al. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113377 · Cited by 2,429 · see citing works
Self-citation
Equilibrium optimizer: A novel optimization algorithm
Faramarzi, A., Heidarinejad, M., Stephens, B., & Mirjalili, S. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105190 · Cited by 2,348 · see citing works
Self-citation
A Cooperative Approach to Particle Swarm Optimization
vandenBergh, F., & Engelbrecht, A. P. (2004) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2004.826069 · Cited by 2,122 · see citing works
Adaptive Particle Swarm Optimization
Zhan, Z., Zhang, J., Li, Y., & Chung, H. S.-H. (2009) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2009.2015956 · Cited by 2,106 · see citing works
The Fully Informed Particle Swarm: Simpler, Maybe Better
Mendes, R., Kennedy, J., & Neves, J. (2004) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2004.826074 · Cited by 1,747 · see citing works
Feature Selection for Classification
Dash, M., & Liu, H. (1997) · Intelligent Data Analysis · doi:10.3233/ida-1997-1302 · Cited by 1,696 · see citing works
Population structure and particle swarm performance
Kennedy, J., & Mendes, R. (2003) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2002.1004493 · Cited by 1,593 · see citing works
A cooperative coevolutionary approach to function optimization
Potter, M. A., & Jong, K. A. D. (1994) · Lecture notes in computer science · doi:10.1007/3-540-58484-6_269 · Cited by 1,519 · see citing works
Computational Intelligence
Engelbrecht, A. P. (2007) · Wiley eBooks · doi:10.1002/9780470512517 · Cited by 1,459 · see citing works
The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation
(2003) · doi:10.1109/cec.1999.781913 · Cited by 1,285 · see citing works
Particle swarm optimization for feature selection in classification: a multi-objective approach
Xue, B., Zhang, M., & Browne, W. N. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tsmcb.2012.2227469 · Cited by 1,282 · see citing works
A Branch and Bound Algorithm for Feature Subset Selection
Narendra, & Fukunaga (1977) · IEEE Transactions on Computers · doi:10.1109/tc.1977.1674939 · Cited by 1,266 · see citing works
Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems
Hashim, F. A., Hussain, K., Houssein, E. H., Mabrouk, M. S., & Al‐Atabany, W. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01893-z · Cited by 1,180 · see citing works
S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization
Mirjalili, S., & Lewis, A. (2012) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2012.09.002 · Cited by 1,104 · see citing works
Self-citation
Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance
Kennedy, J. (2003) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.1999.785509 · Cited by 1,061 · see citing works
Tracking and optimizing dynamic systems with particle swarms
Eberhart, R. C., & Shi, Y. (2002) · Congress on Evolutionary Computation · doi:10.1109/cec.2001.934376 · Cited by 1,018 · see citing works
Gradient-based optimizer: A new metaheuristic optimization algorithm
Ahmadianfar, I., Haddad, O. B., & Chu, X. (2020) · Information Sciences · doi:10.1016/j.ins.2020.06.037 · Cited by 917 · see citing works
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review
Bonyadi, M. R., & Michalewicz, Z. (2016) · Evolutionary Computation · doi:10.1162/evco_r_00180 · Cited by 848 · see citing works
Simulated annealing
van Laarhoven, P. J. M., & Aarts, E. (1987) · Springer eBooks · doi:10.1007/978-94-015-7744-1_2 · Cited by 844 · see citing works
A novel particle swarm optimization algorithm with adaptive inertia weight
Nickabadi, A., Ebadzadeh, M. M., & Safabakhsh, R. (2011) · Applied Soft Computing · doi:10.1016/j.asoc.2011.01.037 · Cited by 811 · see citing works
Particle Swarm Optimization in Wireless-Sensor Networks: A Brief Survey
Kulkarni, R. V., & Venayagamoorthy, G. K. (2010) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2010.2054080 · Cited by 739 · see citing works
BGSA: binary gravitational search algorithm
Rashedi, E., Nezamabadi–pour, H., & Saryazdi, S. (2009) · Natural Computing · doi:10.1007/s11047-009-9175-3 · Cited by 735 · see citing works
Orthogonal Learning Particle Swarm Optimization
Zhan, Z., Zhang, J., Li, Y., & Shi, Y. (2010) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2010.2052054 · Cited by 734 · see citing works
Selection of Relevant Features in Machine Learning.
Langley, P. (1994) · doi:10.21236/ada292575 · Cited by 712 · see citing works
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Chatterjee, A., & Siarry, P. (2004) · Computers & Operations Research · doi:10.1016/j.cor.2004.08.012 · Cited by 695 · see citing works
Computational intelligence PC tools
Eberhart, R., Simpson, P., & Dobbins, R. W. (1996) · Medical Entomology and Zoology · Cited by 676 · see citing works
Particle Swarm Optimization With an Aging Leader and Challengers
Chen, W., Zhang, J., Lin, Y., et al. (2012) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2011.2173577 · Cited by 637 · see citing works
Political Optimizer: A novel socio-inspired meta-heuristic for global optimization
Askari, Q., Younas, I., & Saeed, M. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105709 · Cited by 626 · see citing works
A review of particle swarm optimization. Part I: background and development
Banks, A., Vincent, J., & Anyakoha, C. (2007) · Natural Computing · doi:10.1007/s11047-007-9049-5 · Cited by 624 · see citing works
A hybrid genetic algorithm and particle swarm optimization for multimodal functions
Kao, Y.-T., & Zahara, E. (2007) · Applied Soft Computing · doi:10.1016/j.asoc.2007.07.002 · Cited by 557 · see citing works
Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives
Sengupta, S., Basak, S., & Peters, R. A. (2018) · Machine Learning and Knowledge Extraction · doi:10.3390/make1010010 · Cited by 556 · see citing works
Particle swarm optimiser with neighbourhood operator
Suganthan, P. N. (2003) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.1999.785514 · Cited by 515 · see citing works
Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm
Mohamed, A. W., Hadi, A. A., & Mohamed, A. K. (2019) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-019-01053-x · Cited by 513 · see citing works
Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection
Al-Tashi, Q., Abdulkadir, S. J., Rais, H. M., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2906757 · Cited by 489 · see citing works
Self-citation
Fitness-distance-ratio based particle swarm optimization
Peram, T., Veeramachaneni, K. K., & Mohan, C. K. (2004) · IEEE Swarm Intelligence Symposium · doi:10.1109/sis.2003.1202264 · Cited by 475 · see citing works
A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications
Banks, A., Vincent, J., & Anyakoha, C. (2007) · Natural Computing · doi:10.1007/s11047-007-9050-z · Cited by 472 · see citing works
Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art
Karimi-Mamaghan, M., Mohammadi, M., Meyer, P. E., Karimi-Mamaghan, A. M., & Talbi, E. (2021) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.04.032 · Cited by 466 · see citing works
A novel particle swarm optimization algorithm with Levy flight
Haklı, H., & Uğuz, H. (2014) · Applied Soft Computing · doi:10.1016/j.asoc.2014.06.034 · Cited by 429 · see citing works
A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I
Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., & Coello, C. A. C. (2013) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2013.2290086 · Cited by 423 · see citing works
A Distance-Based Locally Informed Particle Swarm Model for Multimodal Optimization
Qu, B., Suganthan, P. N., & Das, S. (2012) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2012.2203138 · Cited by 422 · see citing works
On benchmarking functions for genetic algorithms
Digalakis, J. G., & Margaritis, K. G. (2001) · International Journal of Computer Mathematics · doi:10.1080/00207160108805080 · Cited by 422 · see citing works
Correlation feature selection based improved-Binary Particle Swarm Optimization for gene selection and cancer classification
Jain, I., Jain, V. K., & Jain, R. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.09.038 · Cited by 422 · see citing works
A survey on swarm intelligence approaches to feature selection in data mining
Nguyen, B. H., Xue, B., & Zhang, M. (2020) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2020.100663 · Cited by 410 · see citing works
Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna
Robinson, J. T., Sinton, S., & Rahmat–Samii, Y. (2003) · Digest - IEEE Antennas and Propagation Society. International Symposium · doi:10.1109/aps.2002.1016311 · Cited by 374 · see citing works
A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy
Moradi, P., & Gholampour, M. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.01.044 · Cited by 372 · see citing works
A review of particle swarm optimization and its applications in Solar Photovoltaic system
Khare, A., & Rangnekar, S. (2012) · Applied Soft Computing · doi:10.1016/j.asoc.2012.11.033 · Cited by 367 · see citing works
Adaptive particle swarm optimization approach for static and dynamic economic load dispatch
Panigrahi, B. K., Pandi, V. R., & Das, S. (2008) · Energy Conversion and Management · doi:10.1016/j.enconman.2007.12.023 · Cited by 364 · see citing works
Population size in Particle Swarm Optimization
Piotrowski, A., Napiorkowski, J. J., & Piotrowska, A. E. (2020) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2020.100718 · Cited by 356 · see citing works
A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition
Mistry, K., Zhang, L., Neoh, S. C., Lim, C. P., & Fielding, B. (2016) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2016.2549639 · Cited by 350 · see citing works
A Novel Sigmoid-Function-Based Adaptive Weighted Particle Swarm Optimizer
Liu, W., Wang, Z., Yuan, Y., et al. (2019) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2925015 · Cited by 350 · see citing works
A modified particle swarm optimizer with dynamic adaptation
Yang, X., Yuan, J., Yuan, J., & Mao, H. (2006) · Applied Mathematics and Computation · doi:10.1016/j.amc.2006.12.045 · Cited by 340 · see citing works
A review on particle swarm optimization algorithm and its variants to clustering high-dimensional data
Esmin, A. A. A., Coelho, R. A., & Matwin, S. (2013) · Artificial Intelligence Review · doi:10.1007/s10462-013-9400-4 · Cited by 339 · see citing works
A novel hybrid PSO-PS optimized fuzzy PI controller for AGC in multi area interconnected power systems
Sahu, R. K., Panda, S., & Gorripotu, T. S. (2014) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2014.08.021 · Cited by 322 · see citing works
A Continuous Genetic Algorithm Designed for the Global Optimization of Multimodal Functions
Chelouah, R., & Siarry, P. (2000) · Journal of Heuristics · doi:10.1023/a:1009626110229 · Cited by 300 · see citing works
Ensemble particle swarm optimizer
Lynn, N., & Suganthan, P. N. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.02.007 · Cited by 298 · see citing works
A hybrid particle swarm optimization algorithm using adaptive learning strategy
Wang, F., Zhang, H., Li, K., et al. (2018) · Information Sciences · doi:10.1016/j.ins.2018.01.027 · Cited by 284 · see citing works
Phasor particle swarm optimization: a simple and efficient variant of PSO
Ghasemi, M., Akbari, E., Rahimnejad, A., et al. (2018) · Soft Computing · doi:10.1007/s00500-018-3536-8 · Cited by 280 · see citing works
A dynamic inertia weight particle swarm optimization algorithm
Jiao, B., Lian, Z., & Gu, X. (2006) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2006.09.063 · Cited by 273 · see citing works
MCPSO: A multi-swarm cooperative particle swarm optimizer
Niu, B., Zhu, Y., He, X., et al. (2006) · Applied Mathematics and Computation · doi:10.1016/j.amc.2006.07.026 · Cited by 273 · see citing works
A review on particle swarm optimization algorithms and their applications to data clustering
Rana, S. S., Jasola, S., & Kumar, R. (2010) · Artificial Intelligence Review · doi:10.1007/s10462-010-9191-9 · Cited by 271 · see citing works
Selective maintenance scheduling under stochastic maintenance quality with multiple maintenance actions
Duan, C., Deng, C., Gharaei, A., Wu, J., & Wang, B. (2018) · International Journal of Production Research · doi:10.1080/00207543.2018.1436789 · Cited by 268 · see citing works
A Review of Particle Swarm Optimization
Jain, N. K., Nangia, U., & Jain, J. (2018) · Journal of The Institution of Engineers (India) Series B · doi:10.1007/s40031-018-0323-y · Cited by 252 · see citing works
Route Planning for Unmanned Aerial Vehicle (UAV) on the Sea Using Hybrid Differential Evolution and Quantum-Behaved Particle Swarm Optimization
Fu, Y., Ding, M., Zhou, C., & Hu, H. (2013) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2013.2248146 · Cited by 246 · see citing works
Energy-Efficient Offloading for DNN-Based Smart IoT Systems in Cloud-Edge Environments
Chen, X., Zhang, J., Lin, B., et al. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2021.3100298 · Cited by 244 · see citing works
A Time-Driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing
Lin, B., Zhu, F., Zhang, J., et al. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2905659 · Cited by 237 · see citing works
Particle swarm optimisation with spatial particle extension
Krink, T., Vesterstrom, J. S., & Riget, J. (2003) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2002.1004460 · Cited by 232 · see citing works
A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization
Nasir, M., Das, S., Maity, D., Sengupta, S., Halder, U., & Suganthan, P. N. (2012) · Information Sciences · doi:10.1016/j.ins.2012.04.028 · Cited by 223 · see citing works
Review on applications of particle swarm optimization in solar energy systems
Elsheikh, A. H., & Elaziz, M. A. (2018) · International Journal of Environmental Science and Technology · doi:10.1007/s13762-018-1970-x · Cited by 221 · see citing works
Chaotic Inertia Weight in Particle Swarm Optimization
Feng, Y., Teng, G., Wang, A.-X., et al. (2007) · doi:10.1109/icicic.2007.209 · Cited by 218 · see citing works
Application of Particle Swarm Optimization to Economic Dispatch Problem: Advantages and Disadvantages
Lee, K., & Park, J.-B. (2006) · IEEE PES Power Systems Conference and Exposition · doi:10.1109/psce.2006.296295 · Cited by 207 · see citing works
A hybrid differential evolution algorithm based on particle swarm optimization for nonconvex economic dispatch problems
Sayah, S., & Hamouda, A. (2013) · Applied Soft Computing · doi:10.1016/j.asoc.2012.12.014 · Cited by 206 · see citing works
A survey on feature selection approaches for clustering
Hançer, E., Xue, B., & Zhang, M. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09800-w · Cited by 205 · see citing works
A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints
Jiang, S., Ji, Z., & Shen, Y. (2013) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2013.10.006 · Cited by 202 · see citing works
On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems
Arumugam, M. S., & Rao, M. V. C. (2007) · Applied Soft Computing · doi:10.1016/j.asoc.2007.01.010 · Cited by 199 · see citing works
A switching delayed PSO optimized extreme learning machine for short-term load forecasting
Zeng, N., Zhang, H., Liu, W., et al. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.01.090 · Cited by 198 · see citing works
A hybrid particle swarm optimizer with sine cosine acceleration coefficients
Chen, K., Zhou, F., Yin, L., et al. (2017) · Information Sciences · doi:10.1016/j.ins.2017.09.015 · Cited by 198 · see citing works
An improved binary particle swarm optimization for unit commitment problem
Yuan, X., Nie, H., Su, A., et al. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.10.047 · Cited by 195 · see citing works
Applications of Particle Swarm Optimization in Geotechnical Engineering: A Comprehensive Review
Hajihassani, M., Armaghani, D. J., & Kalatehjari, R. (2017) · Geotechnical and Geological Engineering · doi:10.1007/s10706-017-0356-z · Cited by 194 · see citing works
Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy
Xin, B., Chen, J., Zhang, J., Fang, H., & Peng, Z. (2011) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2011.2160941 · Cited by 190 · see citing works
Modified binary particle swarm optimization
Lee, S., Soak, S.-M., Oh, S., et al. (2008) · Progress in Natural Science Materials International · doi:10.1016/j.pnsc.2008.03.018 · Cited by 189 · see citing works
A Review of Grey Wolf Optimizer-Based Feature Selection Methods for Classification
Al-Tashi, Q., Rais, H. M., Abdulkadir, S. J., et al. (2019) · Algorithms for intelligent systems · doi:10.1007/978-981-32-9990-0_13 · Cited by 182 · see citing works
Self-citation
Modelling And optimal lot-sizing of the replenishments in constrained, multi-product and bi-objective EPQ models with defective products: Generalised Cross Decomposition
Gharaei, A., Shekarabi, S. A. H., & Karimi, M. (2019) · International Journal of Systems Science Operations & Logistics · doi:10.1080/23302674.2019.1574364 · Cited by 181 · see citing works
Joint Economic Lot-sizing in Multi-product Multi-level Integrated Supply Chains: Generalized Benders Decomposition
Gharaei, A., Karimi, M., & Shekarabi, S. A. H. (2019) · International Journal of Systems Science Operations & Logistics · doi:10.1080/23302674.2019.1585595 · Cited by 177 · see citing works
EMG Feature Selection and Classification Using a Pbest-Guide Binary Particle Swarm Optimization
Too, J., Abdullah, A. R., Saad, N. M., & Tee, W. H. (2019) · Computation · doi:10.3390/computation7010012 · Cited by 176 · see citing works
An expanded particle swarm optimization based on multi-exemplar and forgetting ability
Xia, X., Gui, L., He, G., Wei, B., Zhang, Y., Yu, F., Wu, H., & Zhan, Z. (2019) · Information Sciences · doi:10.1016/j.ins.2019.08.065 · Cited by 175 · see citing works
Particle swarm optimization algorithm and its parameters: A review
Juneja, M., & Nagar, S. K. (2016) · International Conference on Control, Computing, Communication and Materials (ICCCCM) · doi:10.1109/iccccm.2016.7918233 · Cited by 173 · see citing works
A novel multi-swarm particle swarm optimization with dynamic learning strategy
Ye, W., Feng, W., & Fan, S. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.08.051 · Cited by 161 · see citing works
Research on particle swarm optimization: a review
Song, M., & Gu, G. (2005) · doi:10.1109/icmlc.2004.1382171 · Cited by 160 · see citing works
Cost-Driven Off-Loading for DNN-Based Applications Over Cloud, Edge, and End Devices
Lin, B., Huang, Y., Zhang, J., et al. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2961237 · Cited by 159 · see citing works
Watch thy neighbor or how the swarm can learn from its environment
Mendes, R., Kennedy, J., & Neves, J. (2004) · IEEE Swarm Intelligence Symposium · doi:10.1109/sis.2003.1202252 · Cited by 156 · see citing works
Particle swarm optimisation for discrete optimisation problems: a review
Jordehi, A. R., & Jasni, J. (2012) · Artificial Intelligence Review · doi:10.1007/s10462-012-9373-8 · Cited by 156 · see citing works
Task Allocation for Wireless Sensor Network Using Modified Binary Particle Swarm Optimization
Yang, J., Zhang, H., Ling, Y., et al. (2013) · IEEE Sensors Journal · doi:10.1109/jsen.2013.2290433 · Cited by 154 · see citing works
Optimizing multi-objective PSO based feature selection method using a feature elitism mechanism
Amoozegar, M., & Bidgoli, B. M. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.07.013 · Cited by 149 · see citing works
A review on constraint handling strategies in particle swarm optimisation
Jordehi, A. R. (2015) · Neural Computing and Applications · doi:10.1007/s00521-014-1808-5 · Cited by 147 · see citing works
A fractional-order model-based state estimation approach for lithium-ion battery and ultra-capacitor hybrid power source system considering load trajectory
Wang, Y., Gao, G., Li, X., & Chen, Z. (2019) · Journal of Power Sources · doi:10.1016/j.jpowsour.2019.227543 · Cited by 147 · see citing works
A Hamming distance based binary particle swarm optimization (HDBPSO) algorithm for high dimensional feature selection, classification and validation
Banka, H., & Dara, S. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.10.007 · Cited by 146 · see citing works
A Review of Quantum-behaved Particle Swarm Optimization
Fang, W. H., Sun, J. J., Ding, Y., et al. (2010) · IETE Technical Review · doi:10.4103/0256-4602.64601 · Cited by 144 · see citing works
An improved quantum particle swarm optimization algorithm for environmental economic dispatch
Xin-gang, Z., Ji, L., Jin, M., et al. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113370 · Cited by 142 · see citing works
Hybrid simplex search and particle swarm optimization for the global optimization of multimodal functions
Fan, S. S., Liang, Y.-C., & Zahara, E. (2004) · Engineering Optimization · doi:10.1080/0305215041000168521 · Cited by 141 · see citing works
Evolving cognitive and social experience in Particle Swarm Optimization through Differential Evolution: A hybrid approach
Epitropakis, M. G., Plagianakos, V. P., & Vrahatis, M. N. (2012) · Information Sciences · doi:10.1016/j.ins.2012.05.017 · Cited by 139 · see citing works
Particle swarm optimisation in designing parameters of manufacturing processes: A review (2008–2018)
Sibalija, T. V. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105743 · Cited by 136 · see citing works
Hybrid particle swarm optimization and differential evolution for optimal design of water distribution systems
Sedki, A., & Ouazar, D. (2012) · Advanced Engineering Informatics · doi:10.1016/j.aei.2012.03.007 · Cited by 131 · see citing works
A PSO-based multi-objective multi-label feature selection method in classification
Zhang, Y., Gong, D., Sun, X., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-00416-0 · Cited by 131 · see citing works
Particle swarm optimisation (PSO) for allocation of FACTS devices in electric transmission systems: A review
Jordehi, A. R. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.08.007 · Cited by 124 · see citing works
A multi-objective particle swarm optimisation for filter-based feature selection in classification problems
Xue, B., Cervante, L., Shang, L., et al. (2012) · Connection Science · doi:10.1080/09540091.2012.737765 · Cited by 121 · see citing works
Natural Exponential Inertia Weight Strategy in Particle Swarm Optimization
Chen, G., Huang, X., Jia, J., et al. (2006) · World Congress on Intelligent Control and Automation · doi:10.1109/wcica.2006.1713055 · Cited by 121 · see citing works
Improved Random Drift Particle Swarm Optimization With Self-Adaptive Mechanism for Solving the Power Economic Dispatch Problem
El-Sayed, W. T., Hegazy, Y. G., ElBages, M. S., et al. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2695122 · Cited by 120 · see citing works
Solution of an Economic Dispatch Problem Through Particle Swarm Optimization: A Detailed Survey - Part I
Abbas, G., Gu, J. J., Farooq, U., Asad, M. U., & El-Hawary, M. E. (2017) · IEEE Access · doi:10.1109/access.2017.2723862 · Cited by 120 · see citing works
Sustainable supplier selection by a new decision model based on interval-valued fuzzy sets and possibilistic statistical reference point systems under uncertainty
Rabbani, M., Foroozesh, N., Mousavi, S. M., et al. (2017) · International Journal of Systems Science Operations & Logistics · doi:10.1080/23302674.2017.1376232 · Cited by 120 · see citing works
Evolving Ensemble Models for Image Segmentation Using Enhanced Particle Swarm Optimization
Tan, T. Y., Li, Z., Lim, C. P., Fielding, B., Yu, Y., & Anderson, E. (2019) · IEEE Access · doi:10.1109/access.2019.2903015 · Cited by 120 · see citing works
Density-based particle swarm optimization algorithm for data clustering
Alswaitti, M., Albughdadi, M., & Isa, N. A. M. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.08.050 · Cited by 117 · see citing works
Self-citation
Multipopulation cooperative particle swarm optimization with a mixed mutation strategy
Li, W., Meng, X., Huang, Y. P., et al. (2020) · Information Sciences · doi:10.1016/j.ins.2020.02.034 · Cited by 117 · see citing works
Differential mutation and novel social learning particle swarm optimization algorithm
Zhang, X., Wang, X., Kang, Q., et al. (2018) · Information Sciences · doi:10.1016/j.ins.2018.12.030 · Cited by 113 · see citing works
Feature Selection Method Based on Grey Wolf Optimization for Coronary Artery Disease Classification
Al-Tashi, Q., Rais, H. M., & Abdulkadir, S. J. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-99007-1_25 · Cited by 106 · see citing works
Self-citation
A Novel Probability Binary Particle Swarm Optimization Algorithm and Its Application
Wang, L., Wang, X., Fu, J., & Zhen, L. (2008) · Journal of Software · doi:10.4304/jsw.3.9.28-35 · Cited by 105 · see citing works
PSO+: A new particle swarm optimization algorithm for constrained problems
Kohler, M., Vellasco, M. M. B. R., & Tanscheit, R. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105865 · Cited by 104 · see citing works
Bio-Inspired Feature Selection: An Improved Binary Particle Swarm Optimization Approach
Ji, B., Lu, X., Sun, G., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2992752 · Cited by 104 · see citing works
GEPSO: A new generalized particle swarm optimization algorithm
Sedighizadeh, D., Masehian, E., Sedighizadeh, M., et al. (2020) · Mathematics and Computers in Simulation · doi:10.1016/j.matcom.2020.08.013 · Cited by 104 · see citing works
A novel improved particle swarm optimization algorithm based on individual difference evolution
Gou, J., Lei, Y.-X., Guo, W.-P., et al. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.04.025 · Cited by 102 · see citing works
A novel binary gaining–sharing knowledge-based optimization algorithm for feature selection
Agrawal, P., Ganesh, T., & Mohamed, A. W. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05375-8 · Cited by 102 · see citing works
Hybrid PSO-GSA for energy efficient spectrum sensing in cognitive radio network
Eappen, G., & Shankar, T. (2020) · Physical Communication · doi:10.1016/j.phycom.2020.101091 · Cited by 101 · see citing works
Feature selection of unreliable data using an improved multi-objective PSO algorithm
Yong, Z., Dun-wei, G., & Wan-qiu, Z. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.07.057 · Cited by 100 · see citing works
A New Co-Evolution Binary Particle Swarm Optimization with Multiple Inertia Weight Strategy for Feature Selection
Too, J., Abdullah, A. R., & Saad, N. M. (2019) · Informatics · doi:10.3390/informatics6020021 · Cited by 98 · see citing works
A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO
Islam, M. J., Li, X., & Mei, Y. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.04.050 · Cited by 96 · see citing works
HWPSO: A new hybrid whale-particle swarm optimization algorithm and its application in electronic design optimization problems
Laskar, N. M., Guha, K., Chatterjee, I., Chanda, S., Baishnab, K. L., & Paul, P. K. (2018) · Applied Intelligence · doi:10.1007/s10489-018-1247-6 · Cited by 96 · see citing works
CAPSO: Centripetal accelerated particle swarm optimization
Beheshti, Z., & Shamsuddin, S. M. (2013) · Information Sciences · doi:10.1016/j.ins.2013.08.015 · Cited by 95 · see citing works
A Hybrid DA-PSO Optimization Algorithm for Multiobjective Optimal Power Flow Problems
Khunkitti, S., Siritaratiwat, A., Premrudeepreechacharn, S., et al. (2018) · Energies · doi:10.3390/en11092270 · Cited by 94 · see citing works
A Multi-Swarm Self-Adaptive and Cooperative Particle Swarm Optimization
Zhang, J., & Ding, X. (2011) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2011.05.010 · Cited by 93 · see citing works
Particle Swarm Optimization Applications to Mechanical Engineering- A Review
Kulkarni, M. N. K., Patekar, M. S. L., Bhoskar, M. T., et al. (2015) · Materials Today Proceedings · doi:10.1016/j.matpr.2015.07.223 · Cited by 92 · see citing works
Particle Swarm Optimization Algorithm with Exponent Decreasing Inertia Weight and Stochastic Mutation
Li, H., & Gao, Y. (2009) · International Conference on Information and Computing Science (ICIC) · doi:10.1109/icic.2009.24 · Cited by 89 · see citing works
Integrating credit and replenishment policies for deteriorating items under quadratic demand in a three echelon supply chain
Shah, N. H., Chaudhari, U., & Cárdenas‐Barrón, L. E. (2018) · International Journal of Systems Science Operations & Logistics · doi:10.1080/23302674.2018.1487606 · Cited by 88 · see citing works
Optimization of single and multi-areas economic dispatch problems based on evolutionary particle swarm optimization algorithm
Mohammadian, M., Lorestani, A., & Ardehali, M. M. (2018) · Energy · doi:10.1016/j.energy.2018.07.167 · Cited by 88 · see citing works
A reinforcement learning-based communication topology in particle swarm optimization
Xu, Y., & Pi, D. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04527-9 · Cited by 86 · see citing works
Effects of swarm size on Cooperative Particle Swarm Optimisers
van den Bergh, F., & Engelbrecht, A. P. (2001) · Cited by 84 · see citing works
Hybrid approach of improved binary particle swarm optimization and shuffled frog leaping for feature selection
Rajamohana, S. P., & Umamaheswari, K. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2018.02.015 · Cited by 84 · see citing works
Evolutionary multi-objective fault diagnosis of power transformers
Peimankar, A., Weddell, S. J., Jalal, T. S., & Lapthorn, A. (2017) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2017.03.005 · Cited by 83 · see citing works
Particle swarm optimisation for dynamic optimisation problems: a review
Jordehi, A. R. (2014) · Neural Computing and Applications · doi:10.1007/s00521-014-1661-6 · Cited by 81 · see citing works
Particle swarm optimizer with two differential mutation
Chen, Y., Li, L., Peng, H., et al. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.07.020 · Cited by 81 · see citing works
A decreasing inertia weight particle swarm optimizer
Fan, S. S., & Chiu, Y.-Y. (2007) · Engineering Optimization · doi:10.1080/03052150601047362 · Cited by 79 · see citing works
Multi-objective hybrid PSO-APO algorithm based security constrained optimal power flow with wind and thermal generators
Teeparthi, K., & Kumar, D. M. V. (2017) · Engineering Science and Technology an International Journal · doi:10.1016/j.jestch.2017.03.002 · Cited by 79 · see citing works
EMD–PSO–ANFIS‐based hybrid approach for short‐term load forecasting in microgrids
Semero, Y. K., Zhang, J., & Zheng, D. (2019) · IET Generation Transmission & Distribution · doi:10.1049/iet-gtd.2019.0869 · Cited by 79 · see citing works
A Novel U-Shaped Transfer Function for Binary Particle Swarm Optimisation
Mirjalili, S., Zhang, H., Mirjalili, S., Chalup, S. K., & Noman, N. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-3290-0_19 · Cited by 78 · see citing works
Self-citation
A New Adaptive Well-Chosen Inertia Weight Strategy to Automatically Harmonize Global and Local Search Ability in Particle Swarm Optimization
Lei, K., Qiu, Y., & He, Y. (2006) · International Symposium on Systems and Control in Aeronautics and Astronautics · doi:10.1109/isscaa.2006.1627487 · Cited by 77 · see citing works
Optimal Power Flow Using Particle Swarm Optimization of Renewable Hybrid Distributed Generation
Khaled, U., Eltamaly, A. M., & Béroual, A. (2017) · Energies · doi:10.3390/en10071013 · Cited by 77 · see citing works
Particle swarm optimization with crossover: a review and empirical analysis
Engelbrecht, A. P. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-015-9445-7 · Cited by 74 · see citing works
Multi-dimensional particle swarm optimization for robust blind image watermarking using intertwining logistic map and hybrid domain
Kang, X., Chen, Y., Zhao, F., et al. (2019) · Soft Computing · doi:10.1007/s00500-019-04563-6 · Cited by 73 · see citing works
Siting and Sizing of Distributed Generation and Shunt Capacitor Banks in Radial Distribution System Using Constriction Factor Particle Swarm Optimization
Balu, K., & Mukherjee, V. (2020) · Electric Power Components and Systems · doi:10.1080/15325008.2020.1797935 · Cited by 73 · see citing works
Binary particle swarm optimization (BPSO) based state assignment for area minimization of sequential circuits
El‐Maleh, A. H., Sheikh, A. T., & Sait, S. M. (2013) · Applied Soft Computing · doi:10.1016/j.asoc.2013.08.004 · Cited by 71 · see citing works
Solution of an Economic Dispatch Problem Through Particle Swarm Optimization: A Detailed Survey – Part II
Abbas, G., Gu, J. J., Farooq, U., Raza, A., Asad, M. U., & El-Hawary, M. E. (2017) · IEEE Access · doi:10.1109/access.2017.2768522 · Cited by 71 · see citing works
Comparison of population based metaheuristics for feature selection: Application to microarray data classification
Talbi, E.-G., Jourdan, L., García-Nieto, J., et al. (2008) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa.2008.4493515 · Cited by 70 · see citing works
A hybrid non-linear time-varying double-weighted particle swarm optimization for solving non-convex combined environmental economic dispatch problem
Goudarzi, A., Li, Y., & Ji, X. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105894 · Cited by 69 · see citing works
Hybrid particle swarm optimization with simulated annealing
Pan, X., Xue, L., Lu, Y., et al. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6602-4 · Cited by 68 · see citing works
A time-varying mirrored S-shaped transfer function for binary particle swarm optimization
Beheshti, Z. (2019) · Information Sciences · doi:10.1016/j.ins.2019.10.029 · Cited by 68 · see citing works
A Review of Convergence Analysis of Particle Swarm Optimization
Tian, D. (2013) · International Journal of Grid and Distributed Computing · doi:10.14257/ijgdc.2013.6.6.10 · Cited by 67 · see citing works
Single and Multiobjective Optimal Reactive Power Dispatch Based on Hybrid Artificial Physics–Particle Swarm Optimization
Aljohani, T. M., Ebrahim, A. F., & Mohammed, O. A. (2019) · Energies · doi:10.3390/en12122333 · Cited by 66 · see citing works
A Novel Strategy for Optimal PSO Control Parameters Determination for PV Energy Systems
Eltamaly, A. M. (2021) · Sustainability · doi:10.3390/su13021008 · Cited by 66 · see citing works
Unified Particle Swarm Optimization in Dynamic Environments
Parsopoulos, K. E., & Vrahatis, M. N. (2005) · Lecture notes in computer science · doi:10.1007/978-3-540-32003-6_62 · Cited by 64 · see citing works
A novel hybrid algorithm based on particle swarm and ant colony optimization for finding the global minimum
Kıran, M. S., Gündüz, M., & Baykan, Ö. K. (2012) · Applied Mathematics and Computation · doi:10.1016/j.amc.2012.06.078 · Cited by 64 · see citing works
A novel particle swarm optimization based on prey–predator relationship
Zhang, H., Yuan, M., Liang, Y., et al. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.04.008 · Cited by 64 · see citing works
Manipulating Data and Dimension Reduction Methods: Feature Selection
Liu, H., & Zhao, Z. (2011) · Springer eBooks · doi:10.1007/978-1-4614-1800-9_115 · Cited by 63 · see citing works
Optimal parameter regions and the time-dependence of control parameter values for the particle swarm optimization algorithm
Harrison, K. R., Engelbrecht, A. P., & Ombuki-Berman, B. M. (2018) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2018.01.006 · Cited by 63 · see citing works
Grey particle swarm optimization
Leu, M.-S., & Yeh, M. (2012) · Applied Soft Computing · doi:10.1016/j.asoc.2012.04.030 · Cited by 62 · see citing works
A novel hybrid BPSO–SCA approach for feature selection
Kumar, L., & Bharti, K. K. (2019) · Natural Computing · doi:10.1007/s11047-019-09769-z · Cited by 62 · see citing works
Gravitational particle swarm
Tsai, H., Tyan, Y.-Y., Wu, Y.-W., et al. (2013) · Applied Mathematics and Computation · doi:10.1016/j.amc.2013.03.098 · Cited by 60 · see citing works
A Novel Hybrid Ant Colony-Particle Swarm Optimization Techniques Based Tuning STATCOM for Grid Code Compliance
Kamel, O. M., Diab, A. A. Z., Do, T. D., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2976828 · Cited by 56 · see citing works
MPSO: Median-oriented Particle Swarm Optimization
Beheshti, Z., Shamsuddin, S. M., & Hasan, S. (2013) · Applied Mathematics and Computation · doi:10.1016/j.amc.2012.12.013 · Cited by 55 · see citing works
Cultural quantum-behaved particle swarm optimization for environmental/economic dispatch
Liu, T., Jiao, L., Ma, W., et al. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.04.021 · Cited by 54 · see citing works
Multi-objective feature selection for warfarin dose prediction
Sohrabi, M. K., & Tajik, A. (2017) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2017.06.002 · Cited by 54 · see citing works
Optimization of the multi-objective green cyclical inventory routing problem using discrete multi-swarm PSO method
Rau, H., Budiman, S. D., & Widyadana, I. G. A. (2018) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2018.10.006 · Cited by 54 · see citing works
Hybrid Binary Particle Swarm Optimization Differential Evolution-Based Feature Selection for EMG Signals Classification
Too, J., Abdullah, A. R., & Saad, N. M. (2019) · Axioms · doi:10.3390/axioms8030079 · Cited by 53 · see citing works
Battery states online estimation based on exponential decay particle swarm optimization and proportional-integral observer with a hybrid battery model
Yang, X., Chen, Y., Li, B., & Luo, D. (2019) · Energy · doi:10.1016/j.energy.2019.116509 · Cited by 52 · see citing works
Guided dynamic particle swarm optimization for optimizing digital image watermarking in industry applications
Zheng, Z., Saxena, N., Mishra, K. K., et al. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.05.027 · Cited by 51 · see citing works
A multi-information fusion “triple variables with iteration” inertia weight PSO algorithm and its application
Li, M., Chen, H., Shi, X. L., et al. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105677 · Cited by 50 · see citing works
A hybrid binary particle swarm optimization with tabu search for the set-union knapsack problem
Lin, G., Guan, J., Li, Z., et al. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.06.007 · Cited by 49 · see citing works
Detection of Brain Tumor by Particle Swarm Optimization using Image Segmentation
Mahalakshmi, S., & Velmurugan, T. (2015) · Indian Journal of Science and Technology · doi:10.17485/ijst/2015/v8i22/79092 · Cited by 48 · see citing works
A hybrid firefly and particle swarm optimization algorithm applied to multilevel image thresholding
Akan, T., & Ardabili, A. K. (2020) · Multimedia Systems · doi:10.1007/s00530-020-00716-y · Cited by 48 · see citing works
Inertia-Adaptive Particle Swarm Optimizer for Improved Global Search
Suresh, K., Ghosh, S., Kundu, D., et al. (2008) · International Conference on Intelligent Systems Design and Applications (ISDA) · doi:10.1109/isda.2008.199 · Cited by 47 · see citing works
Novel chaotic grouping particle swarm optimization with a dynamic regrouping strategy for solving numerical optimization tasks
Chen, K., Xue, B., Zhang, M., et al. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105568 · Cited by 47 · see citing works
Hybrid particle swarm optimization and pattern search algorithm
Koessler, E. R., & Almomani, A. (2020) · Optimization and Engineering · doi:10.1007/s11081-020-09534-7 · Cited by 47 · see citing works

Flagged first, then by citations

Research works printed but not linked: 10

10.1109/CEC.1999.785511 · DOI
10.7551/mitpress/1090.001.0001 · DOI
Sun, Particle swarm optimization with particles having quantum behavior, 1, 325
Rangaraj, 2017, Implementing energy optimization by a novel hybrid technique for performance improvement in mobile ad hoc network, Int. J. Appl. Eng. Res., 12, 12029
Swesi, 2017, An enhanced binary particle swarm optimization (EBPSO) algorithm based a V-shaped transfer function for feature selection in high dimensional data, Int. J. Adv. Soft Comput. Appl., 9, 1
Liu, 2011, The analysis of binary particle swarm optimization, J. Nanjing Univ., Natural Sci., 47, 504
Liu, Feature selection: An ever evolving frontier in data mining, 4
Jona, 2012, A hybrid swarm optimization approach for feature set reduction in digital mammograms, WSEAS Trans. Inf. Sci. Appl., 9, 340
Zhao, 2018, Application of adaptive chaos PSO algorithm in WSN coverage optimization, Commun. Technol., 1, 10
Rani, 2018, A hybrid medical image coding method based on Haar wavelet transform and particle swarm optimization technique, Int. J. Pure Appl. Math., 118, 3056

Sorted by citations

Enhancing colour image encryption through parameters optimization of memristive hyperchaotic system with CPSO algorithm and LSAIM
Belete, B. A., Gelmecha, D. J., & Singh, R. S. (2025) · The Imaging Science Journal · doi:10.1080/13682199.2025.2454055 · Cited by 9 · see citing works

Authors: Shami, T. M., El-Saleh, A. A., Alswaitti, M., Al-Tashi, Q., Summakieh, M. A., & Mirjalili, S.
Affiliations: University of York (GB)
Department of Electronics and Communication Engineering, College of Engineering, A’Sharqiyah University, Ibra, Oman
Xiamen University Malaysia (MY)
The University of Texas MD Anderson Cancer Center (US)
Multimedia University (MY)
Yonsei University (KR)
ORCID: Shami, Tareq M.
El-Saleh, Ayman A.
Alswaitti, Mohammed
Al-Tashi, Qasem
Summakieh, Mhd Amen
Mirjalili, Seyedali
Venue: IEEE Access
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2022
Volume: 10
Pages: 10031-10061
ISSN: 2169-3536 (ISSN-L)
DOI: 10.1109/access.2022.3142859
OpenAlex: W4205129187
Semantic Scholar: f9de40e3856b26aafd3792120166af0a3f152168
Cited by: 1,433
References: 281
Citations by year: 2026: 322 · 2025: 465 · 2024: 398 · 2023: 202 · 2022: 46
FWCI: 90.74
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/legalcode (vor)
Locations: IEEE Access (publishedVersion, cc-by)
Siti Hasmah Digital Library-MMU Institutiona Repository (Multimedia University) (acceptedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Figshare (submittedVersion, cc-by)
Griffith Research Online (submittedVersion, cc-by)
Funders: Xiamen University Malaysia (XMUM) under the XMUM Research Fund (XMUMRF/2019-C4/IECE/0012)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
Physical Sciences › Engineering › Aerospace Engineering › Antenna Design and Optimization
Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Control Multi-Agent Systems
Keywords: particle swarm optimization, swarm intelligence, premature convergence, PSO variants, hybrid metaheuristic, genetic algorithm hybridization, neighborhood topology, feature selection, engineering applications

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Particle Swarm Optimization: A Comprehensive Survey

Abstract

Integrity signals

Claims the paper makes

Self-check

Cited works that were retracted

Cites

Cited by

Related

Record

About these checks