Fish schools and bird flocks are examples of natural phenomena that served as inspiration for the stochastic optimization method known as particle swarm optimization (PSO). According to the report, PSO has evolved since its introduction in 1995 because of researchers’ constant efforts to enhance and adapt the algorithm to meet different requirements and uses. Beginning with its origins and history, the paper covers several PSO-related subjects. With an emphasis on several topics, such as algorithm structure, parameter selection, topological structure, discrete PSO, parallel PSO, multi-objective optimization PSO, and engineering applications, the article examines the present status of PSO research and applications. This thorough examination demonstrates PSO’s adaptability and broad range of applications in several fields. All things considered, this work seems to be a useful tool for comprehending the theory, history and current state of PSO.

article swarm optimization, discrete swarm optimization, multi-objective optimization, PSO, Inertia Weight

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1. Eberhart RC, Kennedy J. A new optimizer using particle swarm theory. In Proceedings of the sixth international symposium on micro machine and human science. New York, NY, USA: IEEE. 1995;43:6.
2. Eberhart RC, Shi Y. Particle swarm optimization: Developments, applications and resources. In Proceedings of the 2001 congress on evolutionary computation. Piscataway, NJ, USA: IEEE; 2001;1:81–86.
3. P. Engelbrecht. Computational intelligence: An introduction. Wiley; 2007.
4. Kennedy J, Eberhart RC. Particle swarm optimization. In Proceedings of IEEE international conference on neural networks. Perth, Australia. 1995;4:1942–1948.
5. Poli R. An analysis of publications on particle swarm optimization applications. Essex, UK: Department of Computer Science, University of Essex; 2007.
6. Shi Y, Eberhart R. A modified particle swarm optimizer. In Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference. IEEE; 2002. p. 69–73.
7. Abdelbar AM, Abdelshahid S, Wunsch DCI. Fuzzy pso: A generalization of particle swarm optimization. In: Proceedings of 2005 IEEE international joint conference on neural networks (IJCNN ’05) Montreal, Canada; July 31–August 4, 2005. p. 1086–1091.
8. Acan A, Gunay A. Enhanced particle swarm optimization through external memory support. In: Proceedings of 2005 IEEE congress on evolutionary computation, Edinburgh, UK.; Sept 2–4, 2005. p. 1875–1882.
9. Afshinmanesh F, Marandi A, Rahimi-Kian A. A novel binary particle swarm optimization method using artificial immune system. In: Proceedings of the international conference on computer as a tool (EUROCON 2005) Belgrade, Serbia; Nov 21–24, 2005. p. 217–220.
10. Al-kazemi B, Mohan CK. Multi-phase generalization of the particle swarm optimization algorithm. In: Proceedings of 2002 IEEE Congress on Evolutionary Computation, Honolulu, Hawaii. August 7–9, 2002. p. 489– 494.
11. Rifaie MM, Blackwell T. Bare bones particle swarms with jumps ants. Lect Notes Comput Sci Ser. 2012;7461(1):49–60.
12. Ardizzon G, Cavazzini G, Pavesi G. Adaptive acceleration coefficients for a new search diversification strategy in particle swarm optimization algorithms. Inf Sci. 2015;299:337–378.
13. Banka H, Dara S. A hamming distance based binary particle swarm optimization (HDBPSO) algorithm for high dimensional feature selection, classification and validation. 2015.
14. Ivatloo BM. Combined heat and power economic dispatch problem solution using particle swarm optimization with time varying acceleration coefficients. Electr Power Syst Res. 2013;95(1):9–18.
15. Jamian JJ, Mustafa MW, Mokhlis H. Optimal multiple distributed generation output through rank evolutionary particle swarm optimization. Neurocomputing. 2015;152:190–198.
16. Jia D, Zheng G, Qu B, Khan MK. A hybrid particle swarm optimization algorithm for high- dimensional problems. Comput Ind Eng. 2011;61:1117–1122.
17. Jian W, Xue Y, Qian J. An improved particle swarm optimization algorithm with neighborhoods topologies. In: Proceedings of 2004 international conference on machine learning and cybernetics, Shanghai, China; August 26–29, 2004. p. 2332–2337.
18. Jiang CW, Bompard E. A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimization. Math Comput Simul. 2005;68:57–65.

Fish schools and bird flocks are examples of natural phenomena that served as inspiration for the stochastic optimization method known as particle swarm optimization (PSO). According to the report, PSO has evolved since its introduction in 1995 because of researchers’ constant efforts to enhance and adapt the algorithm to meet different requirements and uses. Beginning with its origins and history, the paper covers several PSO-related subjects. With an emphasis on several topics, such as algorithm structure, parameter selection, topological structure, discrete PSO, parallel PSO, multi-objective optimization PSO, and engineering applications, the article examines the present status of PSO research and applications. This thorough examination demonstrates PSO’s adaptability and broad range of applications in several fields. All things considered, this work seems to be a useful tool for comprehending the theory, history and current state of PSO.

article swarm optimization, discrete swarm optimization, multi-objective optimization, PSO, Inertia Weight

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Eberhart RC, Kennedy J. A new optimizer using particle swarm theory. In Proceedings of the sixth international symposium on micro machine and human science. New York, NY, USA: IEEE. 1995;43:6.
2. Eberhart RC, Shi Y. Particle swarm optimization: Developments, applications and resources. In Proceedings of the 2001 congress on evolutionary computation. Piscataway, NJ, USA: IEEE; 2001;1:81–86.
3. P. Engelbrecht. Computational intelligence: An introduction. Wiley; 2007.
4. Kennedy J, Eberhart RC. Particle swarm optimization. In Proceedings of IEEE international conference on neural networks. Perth, Australia. 1995;4:1942–1948.
5. Poli R. An analysis of publications on particle swarm optimization applications. Essex, UK: Department of Computer Science, University of Essex; 2007.
6. Shi Y, Eberhart R. A modified particle swarm optimizer. In Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference. IEEE; 2002. p. 69–73.
7. Abdelbar AM, Abdelshahid S, Wunsch DCI. Fuzzy pso: A generalization of particle swarm optimization. In: Proceedings of 2005 IEEE international joint conference on neural networks (IJCNN ’05) Montreal, Canada; July 31–August 4, 2005. p. 1086–1091.
8. Acan A, Gunay A. Enhanced particle swarm optimization through external memory support. In: Proceedings of 2005 IEEE congress on evolutionary computation, Edinburgh, UK.; Sept 2–4, 2005. p. 1875–1882.
9. Afshinmanesh F, Marandi A, Rahimi-Kian A. A novel binary particle swarm optimization method using artificial immune system. In: Proceedings of the international conference on computer as a tool (EUROCON 2005) Belgrade, Serbia; Nov 21–24, 2005. p. 217–220.
10. Al-kazemi B, Mohan CK. Multi-phase generalization of the particle swarm optimization algorithm. In: Proceedings of 2002 IEEE Congress on Evolutionary Computation, Honolulu, Hawaii. August 7–9, 2002. p. 489– 494.
11. Rifaie MM, Blackwell T. Bare bones particle swarms with jumps ants. Lect Notes Comput Sci Ser. 2012;7461(1):49–60.
12. Ardizzon G, Cavazzini G, Pavesi G. Adaptive acceleration coefficients for a new search diversification strategy in particle swarm optimization algorithms. Inf Sci. 2015;299:337–378.
13. Banka H, Dara S. A hamming distance based binary particle swarm optimization (HDBPSO) algorithm for high dimensional feature selection, classification and validation. 2015.
14. Ivatloo BM. Combined heat and power economic dispatch problem solution using particle swarm optimization with time varying acceleration coefficients. Electr Power Syst Res. 2013;95(1):9–18.
15. Jamian JJ, Mustafa MW, Mokhlis H. Optimal multiple distributed generation output through rank evolutionary particle swarm optimization. Neurocomputing. 2015;152:190–198.
16. Jia D, Zheng G, Qu B, Khan MK. A hybrid particle swarm optimization algorithm for high- dimensional problems. Comput Ind Eng. 2011;61:1117–1122.
17. Jian W, Xue Y, Qian J. An improved particle swarm optimization algorithm with neighborhoods topologies. In: Proceedings of 2004 international conference on machine learning and cybernetics, Shanghai, China; August 26–29, 2004. p. 2332–2337.
18. Jiang CW, Bompard E. A hybrid method of chaotic particle swarm optimization and linear interior for reactive power optimization. Math Comput Simul. 2005;68:57–65.