计算机工程与科学2011，Vol.33Issue(9)：95-99,5.DOI:10.3969/j.issn.1007-130X.2011.09.017
基于新变异算子的改进粒子群优化算法
An Improved Particle Swarm Optimization Algorithm Based on New Mutation Operators
张云明1
作者信息
- 1. 中国人民武装警察部队学院,河北廊坊065000
- 折叠
摘要
Abstract
Particle swarm optimization (PSO) is an optimization algorithm based on swarm intelligence. Based on introducing PSO's theory and flow, this paper analyzes the phenomenon that it suffers from premature convergence, longer search time and lower precision when dealing with complex problems. An improved particle swarm optimization algorithm based on new mutation operators(NMPSO) is presented. The mutation operator is compared with the current particles, and the better one will be selected. So the diversity of population is improved, which can help the algorithm avoid premature convergence efficiently. The comparative simulation results on five benchmark functions verify that NMPSO improves PSO's global search capability, convergence rate and precision.关键词
进化计算/粒子群优化算法/变异算子/全局最优Key words
evolutionary computation/ particle swarm optimization ( PSO) / mutation operator (global optimum分类
信息技术与安全科学引用本文复制引用
张云明..基于新变异算子的改进粒子群优化算法[J].计算机工程与科学,2011,33(9):95-99,5.
