一种快速收敛的非参数粒子群优化算法及其收敛性分析

如何调整粒子群算法的参数引起了大量研究人员的关注.本文提出了一种快速收敛的非参数粒子群优化算法.为了平衡全局搜索和局部搜索，本文算法融合了基于exemplar的学习策略和多交叉操作.根据进一步的稳定性分析，粒子群收敛于搜索空间中的一个固定位置，同时粒子群的位置方差收敛于零点.本文收集了常用的24个准则函数，与7个类似的粒子群算法进行了比较.实验结果表明，本文搜索算法在大部分准则函数上的搜索性能均优于同类算法.同时本文算法在收敛速度上要远优于同类算法.

A Non-parameter Particle Swarm Optimization Algorithm with Fast Convergence Speed and Its Stability Analysis

The adjustment of parameters in particle swarm optimization (PSO) has attracted the attention of many researchers.In the paper,an alternative technology,a non-parameter PSO algorithm with fast convergence speed is proposed.A multi-crossover operation and an exemplar-based learning strategy are combined with the proposed algorithm.According to the first-and second-order stability analyses conducted for the present study,the particle positions are expected to converge at a fixed point in the search space,and the variance of the particle positions converge at zero.In our experiments,we compared the proposed algorithm with 7 other advanced PSO algorithms using 24 widely used benchmark functions.The experimental results indicate that the proposed algorithm yields better solution accuracy than the other PSO algorithms.In particular,the proposed algorithm outperforms the other PSO approaches significantly in terms of the convergence speed.