摘要
提出了一种自适应变异粒子群优化算法,该算法通过遗传变异提高种群多样性的方法使算法增强持续搜索能力,解决了PSO算法的早熟收敛问题。采用标准测试函数进行仿真实验,结果表明:提出的算法具有提高局部最优值的能力,且优化精度更高。
A particle swarm optimization algorithm with adaptive mutation（AMPSO） is presented in this paper.The algorithm of genetic variation through increased population diversity means to make the algorithm of continuing the search capabilities of the PSO algorithm to overcome the phenomenon of premature convergence.Adopting well-known benchmark test functions Rosenbork function simulation experiments,it shows that the proposed algorithm has a strong breakthrough in the capacity of local optimal value and optimal precision.
出处
《皖西学院学报》
2010年第2期27-30,共4页
Journal of West Anhui University
基金
安徽省自然科学基金项目(090412261X)
安徽高校省级自然科学重点项目(KJ2007A072
KJ2007A087)
关键词
粒子群优化
遗传算法
变异
自适应性
particle swam optimization
genetic algorithm
mutation
adaptive
