摘要
对带速度项的PSO算法和不具速度项的动态概率PSO算法进行了随机递推分析,给出了保证收敛的算法的参数取值依据以及相关条件,并基于此提出了改进的动态概率PSO算法(RSPSO)。数值实验分析结果表明,改进的PSO算法能有效避免过早收敛,具有较强的全局搜索能力,且优化能力有了进一步提升.
Based on the theory of stochastic recursion process, this paper studies the convergence of the particle swarmoptimization algorithm, The parameters of the algorithm and the related conditions are given. Then an improved algorithmwith stochastic approximation(RSPSO)is proposed. Simulation results show that RSPSO can effectively avoid the prematureconvergence and possess more powerful global search capabilities, better performance of optimization.
作者
罗金炎
LUO Jinyan(Department of Mathematics, Minjiang University, Fuzhou 350108, China)
出处
《计算机工程与应用》
CSCD
北大核心
2016年第19期25-30,共6页
Computer Engineering and Applications
基金
福建省中青年教师教育科研项目(No.JK2013040)
关键词
粒子群算法
随机递推
过早收敛
随机扰动
Particle Swarm Optimization(PSO)
stochastic recursion
premature convergence
stochastic disturbance
