Convergence proof of an enhanced particle swarm optimisation method integrated with evolutionary game theory

Simple item page
dc.contributor.authorLeboucher, Cedric
dc.contributor.authorShin, Hyo-Sang
dc.contributor.authorSiarry, Patrick
dc.contributor.authorLe Menec, Stephanie
dc.contributor.authorChelouah, Rachid
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2016-08-15T15:37:35Z
dc.date.available2016-08-15T15:37:35Z
dc.date.issued2016-01-08
dc.description.abstractThis paper proposes an enhanced Particle Swarm Optimisation (PSO) algorithm and examines its performance. In the proposed PSO approach, PSO is combined with Evolutionary Game Theory to improve convergence. One of the main challenges of such stochastic optimisation algorithms is the difficulty in the theoretical analysis of the convergence and performance. Therefore, this paper analytically investigates the convergence and performance of the proposed PSO algorithm. The analysis results show that convergence speed of the proposed PSO is superior to that of the Standard PSO approach. This paper also develops another algorithm combining the proposed PSO with the Standard PSO algorithm to mitigate the potential premature convergence issue in the proposed PSO algorithm. The combined approach consists of two types of particles, one follows Standard PSO and the other follows the proposed PSO. This enables exploitation of both diversification of the particles’ exploration and adaptation of the search direction.en_UK
dc.identifier.citationCédric Leboucher, Hyo-Sang Shin, Patrick Siarry, Stéphane Le Ménec, Rachid Chelouah, Antonios Tsourdos, Convergence proof of an enhanced Particle Swarm Optimisation method integrated with Evolutionary Game Theory, Information Sciences, Volumes 346–347, 10 June 2016, Pages 389-411en_UK
dc.identifier.cris2919169
dc.identifier.issn0020-0255
dc.identifier.urihttp://dx.doi.org/10.1016/j.ins.2016.01.011.
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/10306
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-Non-Commercial-No Derivatives 3.0 Unported (CC BY-NC-ND 3.0). You are free to: Share — copy and redistribute the material in any medium or format. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. Information: Non-Commercial — You may not use the material for commercial purposes. No Derivatives — If you remix, transform, or build upon the material, you may not distribute the modified material. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
dc.subjectParticle Swarm Optimisationen_UK
dc.subjectEvolutionary Game Theoryen_UK
dc.subjectLocal optimalityen_UK
dc.subjectConvergence proofen_UK
dc.subjectConvergence speeden_UK
dc.titleConvergence proof of an enhanced particle swarm optimisation method integrated with evolutionary game theoryen_UK
dc.typeArticleen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Convergence_proof_of_an_enhanced_particle_swarm-2017.pdf
Size:
2.89 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.79 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)