浙江大学学报（理学版）2018，Vol.45Issue(3)：261-271,11.DOI:10.3785/j.issn.1008-9497.2018.03.001
基于动态分级和邻域反向学习的改进粒子群算法
An improved particle swarm algorithm based on dynamic segmentation and neighborhood reverse learning
摘要
Abstract
In order to solve the problem that the particle swarm optimization algorithm is likely to fall into local optimum,an improved particle swarm algorithm based on dynamic segmentation and neighborhood reverse learning (DSNRPSO) is proposed.By setting up a dynamic segmentation mechanism,the algorithm divides the particles in the population into three grades,then employs different perturbation strategies for the particles in different grades, so that the particles maintain the evolution to the global optimal direction while the diversity of the population is enhanced.Furthermore,it adopts the method of particle intelligent updating to promote the search ability of particles,and introduces the dynamic neighborhood reverse point enabling a global search to improve the particle searching speed.The preliminary results show that the proposed algorithm has better convergence and stability than several other kinds of optimization algorithms.关键词
粒子群算法/动态分级机制/邻域反向学习/全局搜索策略Key words
particle swarm algorithm/dynamic segmentation mechanism/neighborhood reverse learning/global search strategy分类
信息技术与安全科学引用本文复制引用
任燕芝..基于动态分级和邻域反向学习的改进粒子群算法[J].浙江大学学报（理学版）,2018,45(3):261-271,11.基金项目
国家自然科学基金资助项目(61373174). （61373174）
