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A Novel Hybrid Particle Swarm Optimization Algorithm
Yalan Zhou * #,Jiahai Wang,Jian Yin
Department of Computer Science, Sun Yat-sen University
*Correspondence author
#Submitted by
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Funding: 国家自然科学基金,广东省自然科学基金,广东省科技计划项目,教育部博士点基金,新世纪优秀人才支持计划（No.60573097,05200302、06104916,2005B10101032,20050558017,NCET-06-0727）
Opened online:28 May 2007
Accepted by: none
Citation: Yalan Zhou,Jiahai Wang,Jian Yin.A Novel Hybrid Particle Swarm Optimization Algorithm[OL]. [28 May 2007] http://en.paper.edu.cn/en_releasepaper/content/13087
 
 
In this paper, a framework of hybrid particle swarm optimization algorithm, called HQGPSO, is proposed by reasonably combining the Q-bit evolutionary search of quantum particle swarm optimization (QPSO) algorithm and binary bit evolutionary search of genetic particle swarm optimization (GPSO) in order to achieve better optimization performances. The proposed HQGPSO also can be viewed as a kind of hybridization of micro-space based search and macro-space based search, which enriches the searching behavior to enhance and balance the exploration and exploitation abilities in the whole searching space. To demonstrate its performance, experiments are carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that the proposed algorithm has superior performance to other discrete particle swarm algorithms.
Keywords:quantum particle swarm optimization, genetic particle swarm optimization, hybrid algorithm, knapsack problem, combinatorial optimization problem
 
 
 

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