A new collaborative approach to particle swarm optimization for global optimization

Joong Hoon Kim, Thi Thuy Ngo, Ali Sadollah

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Particle swarm optimization (PSO) is population-based metaheuristic algorithm which mimics animal flocking behavior for food searching and widely applied in various fields. In standard PSO, movement behavior of particles is forced by the current bests, global best and personal best. Despite moving toward the current bests enhances convergence, however, there is a high chance for trapping in local optima. To overcome this local trapping, a new updating equation proposed for particles so-called extraordinary particle swarm optimization (EPSO). The particles in EPSO move toward their own targets selected at each iteration. The targets can be the global best, local bests, or even the worst particle. This approach can make particles jump from local optima. The performance of EPSO has been carried out for unconstrained benchmarks and compared to various optimizers in the literature. The optimization results obtained by the EPSO surpass those of standard PSO and its variants for most of benchmark problems.

Original languageEnglish
Title of host publicationAdvances in Intelligent Systems and Computing
PublisherSpringer Verlag
Pages641-649
Number of pages9
Volume437
ISBN (Print)9789811004506
DOIs
Publication statusPublished - 2016
Event5th International Conference on Soft Computing for Problem Solving, SocProS 2015 - Roorkee, India
Duration: 2015 Dec 182015 Dec 20

Publication series

NameAdvances in Intelligent Systems and Computing
Volume437
ISSN (Print)21945357

Other

Other5th International Conference on Soft Computing for Problem Solving, SocProS 2015
CountryIndia
CityRoorkee
Period15/12/1815/12/20

Fingerprint

Global optimization
Particle swarm optimization (PSO)
Animals

Keywords

  • Extraordinary particle swarm optimization
  • Global optimization
  • Metaheuristics
  • Particle swarm optimization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Kim, J. H., Ngo, T. T., & Sadollah, A. (2016). A new collaborative approach to particle swarm optimization for global optimization. In Advances in Intelligent Systems and Computing (Vol. 437, pp. 641-649). (Advances in Intelligent Systems and Computing; Vol. 437). Springer Verlag. https://doi.org/10.1007/978-981-10-0451-3_57

A new collaborative approach to particle swarm optimization for global optimization. / Kim, Joong Hoon; Ngo, Thi Thuy; Sadollah, Ali.

Advances in Intelligent Systems and Computing. Vol. 437 Springer Verlag, 2016. p. 641-649 (Advances in Intelligent Systems and Computing; Vol. 437).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kim, JH, Ngo, TT & Sadollah, A 2016, A new collaborative approach to particle swarm optimization for global optimization. in Advances in Intelligent Systems and Computing. vol. 437, Advances in Intelligent Systems and Computing, vol. 437, Springer Verlag, pp. 641-649, 5th International Conference on Soft Computing for Problem Solving, SocProS 2015, Roorkee, India, 15/12/18. https://doi.org/10.1007/978-981-10-0451-3_57
Kim JH, Ngo TT, Sadollah A. A new collaborative approach to particle swarm optimization for global optimization. In Advances in Intelligent Systems and Computing. Vol. 437. Springer Verlag. 2016. p. 641-649. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-981-10-0451-3_57
Kim, Joong Hoon ; Ngo, Thi Thuy ; Sadollah, Ali. / A new collaborative approach to particle swarm optimization for global optimization. Advances in Intelligent Systems and Computing. Vol. 437 Springer Verlag, 2016. pp. 641-649 (Advances in Intelligent Systems and Computing).
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