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Reseach Article

Particle Swarm Optimization: A Study of Variants and Their Applications

by Ashok Kumar, Brajesh Kumar Singh, B. D. K. Patro
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 135 - Number 5
Year of Publication: 2016
Authors: Ashok Kumar, Brajesh Kumar Singh, B. D. K. Patro

Ashok Kumar, Brajesh Kumar Singh, B. D. K. Patro . Particle Swarm Optimization: A Study of Variants and Their Applications. International Journal of Computer Applications. 135, 5 ( February 2016), 24-30. DOI=10.5120/ijca2016908406

@article{ 10.5120/ijca2016908406,
author = { Ashok Kumar, Brajesh Kumar Singh, B. D. K. Patro },
title = { Particle Swarm Optimization: A Study of Variants and Their Applications },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 135 },
number = { 5 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 24-30 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016908406 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:35:47.855137+05:30
%A Ashok Kumar
%A Brajesh Kumar Singh
%A B. D. K. Patro
%T Particle Swarm Optimization: A Study of Variants and Their Applications
%J International Journal of Computer Applications
%@ 0975-8887
%V 135
%N 5
%P 24-30
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

In order to improve the performance of PSO algorithm, number of its variants has been made. This paper presents detail overview of the basic concepts of PSO and its variants. Many variants of PSO have been developed due to improved speed of convergence and quality of solution found by Researchers. The Applications of PSO in Complex Environments is discussed. Modifications, both those already developed, and promising future application areas are reviewed. Observation and review of 117 related studies in the period between 1995 and 2015 on different variants of PSO algorithms are discussed along with their advantages and disadvantages.

  1. R. C. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” Proceedings of the 6th International Symp. on Micromachine and Human Science., Nagoya, Japan, pp. 39–43,1995.
  2. J. Kennedy and R. Eberhart, "Particle Swarm Optimization", Proceedings of IEEE International Conference on Neural Networks, vol.4, pp. 1942-1948, Piscataway, 1995.
  3. A.P.Engelbrecht,“Fundamental of Computational Swarm Inteligent,” First ed. The atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England: John Wiley & Sons Ltd, 2005.
  4. B. Santosa, "Tutorial Particle Swarm Optimization," 2006.
  5. M. B. Ghalia, "Particle Swarm Optimization with an Improved Exploration-Exploitation Balance," Proceedings of 51st Midwest symposium on circuits and systems(MWSCAS’08),pp.759-762,USA,August 2008.
  6. Eberhart,R.C.and Shi,Y.”Particle swarm optimization: developments, applications and reseources.” Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2001),vol.1, pp. 81-86,seoul,south korea,May2001.
  7. Qinghai. Bai, "Analysis of Particle Swarm Optimization Algorithm," Computer and Information Science, vol. 3,no.1, pp.180, Februari 2010.
  8. H.Y.Fan, "A modification to particle swarm optimization algorithm," Engineering Computations vol.19,no.8, pp. 970-989,2002.
  9. Y. Shi and R. C. Eberhart, “A modified particle swarm optimizer,” Proceedings of IEEE International conference on world Congress on Evolutionary Computation(ICEC’98),., pp. 69–73, USA,May 1998.
  10. Ozcan, Ender, and Chilukuri K. Mohan. "Partial shape matching using genetic algorithms.” Pattern Recognition Letters vol.18,no.10, pp. 987-992,1997.
  11. M. Clerc and J. Kennedy, "The Particle Swarm - Explosion, Stability, and Convergence in a Multidimensional Complex Space", IEEE Transactions on Evolutionary Computation, vol. 6,no.1, pp. 58-73, 2002.
  12. Wang, Hui, Yong Liu, and SanyouZeng. "A hybrid particle swarm algorithm with Cauchy mutation." Proceedings of IEEE Swarm Intelligence Symposium(SIS’07),pp. 356-360,Honolulu,Hawaii,USA,April 2007.
  13. Settles, Matthew, and Terence Soule. "Breeding swarms: a GA-PSO hybrid,” Proceedings of the 2005 conference on Genetic and evolutionary computation, ACM, 2005.
  14. R.Thangaraj, M.Pant,A.Abraham,P.Bouvry, "Particle swarm optimization: Hybridization perspectives and experimental illustrations". Applied Mathematics and Computation ,vol. 217,pp. 5208-5226, 2011.
  15. Premalatha. K., and A. M. Natarajan. "Hybrid PSO and GA for global maximization," International Journal Open Problems Compt. Math., vol.2,no.4, pp. 597-608,December 2009.
  16. K. Parsopoulos and M. Vrahatis, “Recent approaches to global optimization problems through particle swarm optimization”, Natural Computing, vol. 1, pp. 235-306, May 2002.
  17. E. Laskari, K. Parsopoulos, and M. Vrahatis, “Particle swarm optimization for integerprogramming”, in Proceedings IEEE Congr.Evol.Comput., vol. 2, pp. 1582-1587, 2002.
  18. H. Shayeghi, M. Mahdavi, A. Bagheri, “Discrete PSO algorithm based optimization of transmission lines loading in TNEP problem, Energy Conversion and Management,” vol. 51, no. 1, pp. 112-121, January 2010.
  19. Li-Yeh Chuang, Hsueh-Wei Chang, Chung-JuiTu, Cheng-Hong Yang, “Improved binary PSO for feature selection using gene expression data,” Computational Biology and Chemistry, vol. 32, no. 1, pp.29-38, 2008.
  20. M. FatihTaşgetiren, Yun-Chia Liang, “A Binary Particle Swarm Optimization Algorithm for Lot Sizing Problem,” Journal of Economic and Social Research vol. 5 , no.2,pp. 1-20, 2003.
  21. Tao Gong, Andrew L Tuson, ”Binary particle swarm optimization: a formal analysis approach,” Proceedings of the 9th annual conference on Genetic and evolutionary computation (GECCO '07), pp. 172-172, ACM New York, USA, 2007.
  22. Mohamad, M.S., Omatu, S., Deris, S., yoshioka,M., ”A modified binary particle swarm optimization for selecting the small subset of informative genes from gene expression data,” IEEE Trans. on Information Biomedicine,vol.15,no.6,pp.813-822, 2011.
  23. Kennedy, J and Eberhart, R.C. “A discrete binary version of the particle swarm algorithm”, IEEE International Conference on Systems, Man, and Cybernetics, vol.5,pp. 4104-4108, 1997.
  24. F.Van den Bergh. ,"An Analysis of Particle Swarm Optimizers," Department of Computer Science, PhD thesis, University of Pretoria, South Africa, 2002.
  25. Narinder Singh, S.B. Singh, "Personal best position particle swarm opti-mization," Journal of Applied Computer Science and Mathematics, vol. 12 ,no.6,pp.69-76, 2012.
  26. swarm optimization.
  27. Brits, Riaan, Andries P. Engelbrecht, and F. Van den Bergh. "A niching particle swarm optimizer." Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning. Vol. 2, pp. 692-696,Singapore, 2002.
  28. A.P Engelbrecht and L.N.H. van Loggerenberg, ”Enhancing the Niche PSO,” Proceedings of IEEE Congress on Evolutionary Computation (CEC’07), pp. 2297-2302,Singapore,September 2007.
  29. S.Bird, X. Li., "Adaptively choosing niching parameters in a PSO,"  Proceedings of the 8th annual conference on Genetic and evolutionary computation(GECCO’06), ACM, pp.3-9,July 2006.
  30. Evers, George I., and M. Ben Ghalia, "Regrouping particle swarm optimization: a new global optimization algorithm with improved performance consistency across benchmarks",  Proceedings of IEEE International Conference on Systems, Man and Cybernetics (SMC’09),pp.3901-3908 October 2009.
  31. M. Dorigo, T. Stützle, “Ant Colony Optimization,” MIT Press, Cambridge, MA, 2004.
  32. Yao, Jingzheng, and Duanfeng Han,"Improved barebones particle swarm optimization with neighborhood search and its application on ship design," Mathematical Problems in Engineering, 2013.
  33. Kang, Fei, Junjie Li, and Sheng Liu. "Combined data with particle swarm optimization for structural damage detection," Mathematical Problems in Engineering, 2013.
  34. Szabo, Alexandre, and Leandro Nunes de Castro. "A Constructive Data Classification Version of the Particle Swarm Optimization Algorithm," Mathematical Problems in Engineering, 2013.
  35. Zhou, Di, Jun Sun, and WenboXu., "An advanced quantum-behaved particle swarm optimization algorithm utilizing cooperative strategy." Proceedings of IEEE Third International Workshop on Advanced Computational Intelligence(IWAC’10),pp. 344-349,Suzhou,Jiangsu,August 2010.
  36. Coello Coello,C.A. and Lechuga,M.S, ”MOPSO: a proposal for multiple objective particle swarm optimization,” Proceedings of the IEEE Congress on Evolutionary Computation(CEC’02),pp.1051-1056,vol.2, Honolulu, Hawaii USA,May 2002.
  37. Hu,X .and Eberhart, R.C., “Multiobjective optimization using dynamic neighborhood particle swarm optimization,” Proceedings of the IEEE Congress on Evolutionary Computation(CEC’02),vol. 2, pp. 1677-1681,Honolulu, Hawaii USA, May 2002.
  38. Fieldsend, J.E. and Singh, S.A, “multi-objective algorithm based upon particle swarm optimization, an efficient data structure and turbulence,” Proceedings of the Workshop on Computational Intelligence,. pp. 37- 44, Birmingham, UK, 2002.
  39. Hu,X., Eberhart,R.C., and Shi,Y., “Particle swarm with extended memory for multiobjective optimization,”Proceedings of the IEEE Swarm Intelligence Symposium(SIS’03), pp. 193-197, Indianapolis, Indian, USA.,April 2003.
  40. Li,X., “A non-dominated sorting particle swarm optimizer for multiobjective optimization,” Lecture Notes in Computer Science, Proceedings of the Genetic and Evolutionary Computation Conference, pp.37-48, USA ,2003.
  41. Yen,G.G, Lu,H.,”Dynamic population strategy assisted particle swarm optimization in multiobjective evolutionary algorithm design,” Proceedings of the IEEE International Symposium on Intelligent Control (ISIC’03),pp.697-702, Houston,Texas,USA, October 2003.
  42. Moore, J. and Chapman, R., “Application of particle swarm to multi-objective optimization,” Department of Computer Science and Software Engineering, Auburn University,1999.
  43. Mostaghim,S. and Teich, J., “Strategies for finding local guides in multi-objective particle swarm optimization (MOPSO),” Proceedings of the IEEE Swarm Intelligence Symposium(SIS’03), pp. 193-197 Indianapolis,IN,USA,April 2003.
  44. K.E.Parsopoulos and M.N.Vrahatis,“Particle swarm optimization method in multiobjective problems,” Proceedings of the ACM Symposium on Applied Computing, pp. 603-607, 2002.
  45. Ray,T. and Liew,K.M, “ A swarm metaphor for multiobjective design optimization,” Engineering Optimization, vol. 34 ,no. 2, pp. 141-153, 2002.
  46. Hu, X., Eberhart, R.C.,”Solving constrained nonlinear optimization problems with particle swarm optimization,” Proceedings of the Sixth World Multiconference on Systemics Cybernetics and Informatics, Orlando,USA, 2002.
  47. Y. Shi , R. C.and Eberhart, “Tracking and optimizing dynamic systems with particle swarms,” Proceedings of the IEEE Congresson Evolutionary Computation ,pp. 94-97, Seoul,Korea 2001.
  48. Hu, X., Eberhart, R.C.,”Adaptive particle swarm optimization:detection and response to dynamic systems,” Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1666-1670, Honolulu,Hawaii USA, 2002.
  49. Brajesh Kumar Singh, A. K. Misra, “Software Effort Estimation by Genetic Algorithm Tuned Parameters of Modified Constructive Cost Model for NASA Software Projects,” International Journal of Computer Applications, Volume 59 No.9,pp.22-26,December2012.
  50. Rini,D.P., Shamsuddin.S.M. and Yuhaniz,S.S.,”Particle Swarm Optimization: Technique,System and Challenges”, International Journal of Computer Applications, Volume 14, No.14,pp.19-27, January 2011.
Index Terms

Computer Science
Information Sciences


Particle Swarm Optimization (PSO) Basic PSO Modification PSO Bird Flocking Evolutionary Optimization Biologically Inspired Computational Search.