计算机科学与探索Issue(4)：506-512,7.DOI:10.3778/j.issn.1673-9418.1312014
压缩因子综合信息粒子群算法
Comprehensive Informed Particle Swarm Optimizer Based on Constrict Factor
摘要
Abstract
The diversity of swarm will be impaired in late period of evolution for a swarm intelligent algorithm and the convergence of each individual element is enhanced, so the major disadvantage of particle swarm optimizer is vulnerable to be trapped in the local optima. This paper proposes a new variant particle swarm optimizer which com-bines constrict factor and comprehensive informed strategy. The constrict factor can balance the global and local models, and comprehensive informed strategy can efficiently enhance the diversity of all particles. By comparing the standard particle swarm optimizer, adaptive particle swarm optimizer and particle swarm optimizer based on con-strict factor on 7 test functions with accuracy level, success rate and convergence velocity, the results show that the new algorithm can obtain a higher accurate level and faster convergence velocity.关键词
综合信息策略/压缩因子/粒子群算法Key words
comprehensive informed/constrict factor/particle swarm optimizer分类
信息技术与安全科学引用本文复制引用
张成兴..压缩因子综合信息粒子群算法[J].计算机科学与探索,2014,(4):506-512,7.基金项目
The 2012 Western and Boundary Areas Planning Foundation of Ministry of Education of China under Grant No.12XJA910002(教育部2012年度西部和边疆地区规划基金项目) （教育部2012年度西部和边疆地区规划基金项目）
