基于粒子滤波重采样与变异操作的改进粒子群算法

针对标准粒子群优化(PSO)算法在求解过程中存在求解精度低、搜索后期收敛速度慢等问题,提出一种基于粒子滤波重采样步骤与变异操作相结合的改进PSO算法——RSPSO。该算法充分利用重采样中具有较大权值的粒子被保留和复制、较小权值的粒子被舍弃的特点,并利用已有的变异操作方法克服粒子匮乏的缺点,大大增强了PSO算法中后期搜索阶段的局部搜索能力。在不同基准函数下对RSPSO算法和标准PSO算法以及文献中其他改进算法进行对比。实验结果表明, RSPSO算法的收敛速度较快,同时其搜索精度和解的稳定性均有所提高,且能够全局地解决多峰问题。

Improved particle swarm optimization based on re-sampling of particle filter and mutation

Concerning the low accuracy and convergence of standard Particle Swarm Optimization (PSO) algorithm, an improved particle swarm optimization based on particle filter re-sampling and mutation named RSPSO was proposed. By using the resampling characteristic of abandoning particles with low weights and duplicating and retaining particles with high weights, an existing method for mutation was adopted to overcome the disadvantage of particle degeneracy, which greatly enhanced the local search capability in the later searching stage of PSO algorithm. RSPSO algorithm was compared with the standard algorithm and some other improved algorithms in the literature under different benchmark functions. The experimental results show that RSPSO has faster convergence, higher accuracy and better stability, and it is able to solve multi-modal problems globally.