一种改进的小波变异粒子群优化算法

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。

An Improved Particle Swarm Optimization Algorithm with Wavelet Mutation

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.