采用种群进化的粒子群多模态函数优化

王栋浩 靳其兵 牛亚旭

摘  要： 针对多模态问题中收敛速度慢，粒子种群容易早熟的问题，提出一种利用种群进化的改进粒子群算法（SRPSO）。该算法在经典多模态粒子群优化算法SPSO的基础上，通过对初始种群进行均匀化空间拉伸更新，同时，对每个新粒子進行梯度进化，加快了粒子种群收敛速度。为了避免种群早熟，漏掉部分极值点，引入环形拓扑模型提高种群交流能力，同时对速度更新公式做出改进。最后利用6个经典的测试函数对三种经典算法做对比实验，结果表明SRPSO具有加快收敛速度，提高寻优成功率的性能。

关键词： 多模态函数; 粒子群算法; 小生境技术; 群智能; 环形拓扑; 粒子梯度进化

中图分类号： TN911.1?34                      文献标识码： A                           文章编号： 1004?373X（2020）13?0106?04

Particle swarm multi?modal function optimization adopting population evolution

WANG Donghao， JIN Qibing， NIU Yaxu

（College of Information Science & Technology， Beijing University of Chemical Technology， Beijing 100029， China）

Abstract： In view of the multi?modal related problems like slow convergence rate and particle population being prone to premature， a species ring?topology particle swarm optimization （SRPSO） is proposed. On the basis of the classical multi?modal species?based PSO （SPSO） algorithm， the proposed algorithm accelerates the convergence rate of the particle population by uniformization space stretching and updating of the initial population and gradient evolution of each new particle. In order to avoid population premature and missing some extreme points， a ring topology model is introduced to improve the communication ability of the population. Meanwhile， the speed updating formula is improved. The contrastive experiments were performed on the three classical algorithms by six classical test functions. The results show that SRPSO has the performance of accelerating the convergence rate and improving the success rate of optimization.

Keywords： multi?modal function; PSO algorithm; niche technology; swarm intelligence; ring topology; particle gradient evolution