Hybrid swarm intelligent parallel algorithm research based on multi-core clusters

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• 作者：Wenjing Li^a ; ^{liwj@gxtc.edu.cn}Author Vitae ; Yingzhou Bi^a ; ^bAuthor Vitae ; Xiaofeng Zhu^cAuthor Vitae ; Chang-an Yuan^aAuthor Vitae ; Xiang-bo Zhang^bAuthor Vitae
• 关键词：Artificial fish swarm algorithm ; Artificial bee colony swarm algorithm ; Optimization ; Hybrid swarm intelligent algorithm ; Parallel algorithm
• 刊名：Microprocessors and Microsystems
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：47
• 期：part_PA
• 页码：151-160
• 全文大小：1316 K
• 卷排序：47

文摘

In order to solve poor fine searching capacity of artificial fish swarm algorithm and artificial bee colony swarm algorithm in late state to result in insufficient local optimization, hybrid swarm intelligent parallel algorithm research based on multi-core clusters is proposed; Then, reverse learning mechanism is introduced in early stage of algorithm, initialized swarms are evenly distributed, and swarms are randomly divided into two groups to make interactive learning strategy accelerates rate of convergence, and basic artificial fish swarm algorithm and artificial bee colony swarm algorithm are used to make global searching. In late stage of algorithm, niches artificial fish swarm algorithm and Random Perturbation Artificial Bee Colony are used to make local fine searching to the solution obtained in early stage; On this basis, MPI+OpenMP+STM parallel programming model based on multi-core clusters is established for parallel design and analysis. Finally, stimulation experiment indicates optimizing efficiency of this algorithm is higher than single artificial fish swarm algorithm and artificial bee colony swarm algorithm.