摘要
为提高粒子群优化(PSO)算法的优化性能,提出一种改进的小波变异粒子群算法(IPSOWM)。在每次迭代时以一定的概率选中粒子进行小波变异扰动,从而克服PSO算法后期易发生早熟收敛和陷入局部最优的缺点。数值仿真结果表明,IPSOWM算法的搜索精度、收敛速度及稳定性均优于PSO和PSOWM算法。
Particle Swarm Optimization（PSO） algorithm is difficult to deal with the problems of premature and local convergence. In order to solve the problems, an Improved PSO with Wavelet Mutation（IPSOWM） algorithm is proposed. In IPSOWM, mutation operator is undertaken by selecting particles with certain small probability so as to overcome the PSO＇s drawback of occurring premature convergence and trapping in the local optima. Experimental results on benchmark fimctions show that the performance of IPSOWM algorithm is obviously superior to that of the other PSO algorithms in references, including convergence precision, convergence rate and stability.
出处
《计算机工程》
CAS
CSCD
2012年第21期145-147,共3页
Computer Engineering
基金
国家部委基金资助项目
江苏省高校自然科学基础研究基金资助项目(07KJB510032)
江苏省普通高校研究生科研创新计划基金资助项目(CX10S_007Z)
关键词
粒子群优化算法
小波变异
小波变异粒子群优化算法
全局最优
鲁棒性
Particle Swarm Optimization（PSO） algorithm
wavelet mutation
PSO algorithm with wavelet mutation
global optimal
robustness
