Convergence proof of an enhanced Particle Swarm Optimisation method integrated with Evolutionary Game Theory

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• 作者：Cé ; dric Leboucher^a ; Hyo-Sang Shin ; ^b ; ^{h.shin@cranfield.ac.uk" class="auth_mail" title="E-mail the corresponding author} ; ^{hyosang.s.shin@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Patrick Siarry^c ; Sté ; phane Le Mé ; nec^a ; Rachid Chelouah^d ; Antonios Tsourdos^b
• 关键词：Particle Swarm Optimisation ; Evolutionary Game Theory ; Local optimality ; Convergence proof ; Convergence speed
• 刊名：Information Sciences
• 出版年：2016
• 出版时间：10 June 2016
• 年：2016
• 卷：346-347
• 期：Complete
• 页码：389-411
• 全文大小：2532 K

文摘

This 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.