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《Journal of Nanjing University of Science and Technology》 2017-03
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Multi-strategy adaptive particle swarm optimization algorithm

Tang Kezong;Feng Jianwen;Li Fang;Yang Jingyu;School of Information Engineering,Jingdezhen Ceramic Institute;Key Laboratory of Intelligent Perception and Systems for High-dimensional Information of Ministry of Education,Nanjing University of Science and Technology;  
In order to improve the efficiency of particle swarm optimization( PSO) algorithm for searching for optimal solutions,a multi-strategy adaptive particle swarm optimization( MAPSO) algorithm is proposed.A diversity-measurement strategy is developed to evaluate the population distribution.A real-time alternating strategy is performed to determine predefined evolutionary states,exploration or exploitation. During iterative optimization,the inertia weight is dynamically controlledaccording to the diversity of particles.An elitist learning strategy is introduced to enhance population diversity and to prevent the population from possibly falling into local optimal solutions.Experimental results show that,compared with the adaptive particle swarm optimization( APSO),comprehensive learning particle swarm optimization( CLPSO) and perturbed particle swarm optimization( PPSO),the MAPSO can substantially enhance the ability of jumping out of the local optimal solutions and significantly improve the search efficiency and convergence speed.
【Key Words】: particle swarm optimization diversity-measurement real-time alternating strategy elitist learning strategy population diversity
【Fund】: 国家自然科学基金(61662037);; 高维信息智能感知与系统教育部重点实验室开放课题资助课题(JYB201507);; 江西省科技计划项目(20161BAB212042);; 江西省教育厅科学技术研究项目(GJJ150927)
【CateGory Index】: TP18
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