Diversity enhanced particle swarm optimization with neighborhood search

详细信息    查看全文

• 作者：Hui Wang^a ; ^{wanghui_cug@yahoo.com.cn} ; Hui Sun^a ; ^{sunhui2006@yahoo.com.cn} ; Changhe Li^b ; ^{cl160@mcs.le.ac.uk} ; Shahryar Rahnamayan^c ; ^{shahryar.rahnamayan@uoit.ca} ; Jeng-shyang Pan^a ; ^d ; ^e ; ^{jspan@cc.kuas.edu.tw}
• 关键词：Particle Swarm Optimization (PSO) ; Diversity ; Neighborhood search ; Global optimization
• 刊名：Information Sciences
• 出版年：2013
• 出版时间：20 February, 2013
• 年：2013
• 卷：223
• 期：Complete
• 页码：119-135
• 全文大小：547 K

文摘

Particle Swarm Optimization (PSO) has shown an effective performance for solving variant benchmark and real-world optimization problems. However, it suffers from premature convergence because of quick losing of diversity. In order to enhance its performance, this paper proposes a hybrid PSO algorithm, called DNSPSO, which employs a diversity enhancing mechanism and neighborhood search strategies to achieve a trade-off between exploration and exploitation abilities. A comprehensive experimental study is conducted on a set of benchmark functions, including rotated multimodal and shifted high-dimensional problems. Comparison results show that DNSPSO obtains a promising performance on the majority of the test problems.