摘要
以保证全局收敛的随机微粒群算法SPSO为基础,提出了一种改进的随机微粒群算法——GAT-SPSO。该方法是在SPSO的进化过程中,以锦标赛选择机制下的遗传算法所产生的最优个体来代替SPSO中停止的微粒,参与下一代的群体进化。通过对三个多峰的测试函数进行仿真,其结果表明:在搜索空间维数相同的情况下,GAT-SPSO的收敛率及收敛速度均大大优于SPSO。
Based on the stochastic particle swarm optimization algorithm that guarantees global convergence,an improved stochastic particle swarm optimization algorithm-GAT-SPSO is proposed.During the evolution of SPSO,the best particle produced by genetic algorithm of tournament selection substitutes for the stopping particle and takes part in the evolution of next generation. Through the experiments of three muhi-modal test functions,the result of simulation proves that the speed of convergence and the rate of convergence for GAT-SPSO are better than SPSO at the same dimension of search space.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第4期51-53,84,共4页
Computer Engineering and Applications
基金
国家教育部重点科技项目(the Key Technologies Project of the Ministry of Education of China No.204018)。
关键词
随机微粒群算法
遗传算法
锦标赛选择
全局优化
Stochastic Particle Swarm Optimization（SPSO）
Genetic Algorithm （GA）
tournament selection
global optimization
