摘要
提出了一种改进的混沌粒子群优化混合算法.该算法利用信息交换机制将两组种群分别用差分进化算法和粒子群算法进行协同进化,并且将混沌变异操作引入其中,加强算法的局部搜索能力.通过对3个标准函数进行测试,仿真结果表明该算法与差分进化粒子群优化(DEPSO)算法相比,全局搜索能力和抗早熟收敛性能大大提高.
A hybrid algorithm of the improved chaotic particle swarm optimization is proposed. Based on the information exchange mechanism, the algorithm uses differential evolution algorithm and particle swarm algorithm to make co-evolution for two groups of populations, and the chaos mutation is introduced into the algorithm to enhance the efficiency of local search capabilities. Using three standard functions to test it, simulation results show that, compared with the differential evolution and particle swarm optimization （DEPSO） algorithm, global search ability and resistance to premature convergence are increased greatly.
出处
《应用科技》
CAS
2012年第1期5-8,共4页
Applied Science and Technology
基金
国家自然科学基金资助项目(60874069)
国家863计划资助项目(2009AA04Z124
2009AA04Z137)
关键词
混合算法
差分进化
粒子群优化
协同进化
混沌变异
早熟收敛
hybrid algorithm
differential evolution optimization
particle swarm
co-evolution
chaotic variation
premature convergence
