基于粒子群优化的适应Memetic算法分析

doi:10.6040/j.issn.1671-9352.7.2014.002

山东大学学报（理学版） ›› 2014, Vol. 49 ›› Issue (08): 118-124.doi: 10.6040/j.issn.1671-9352.7.2014.002

• 论文 • 上一篇

基于粒子群优化的适应Memetic算法分析

曲滨鹏¹, 王智昊²

1. 山东医学高等专科学校卫生管理系, 山东济南 250002;
2. 山东师范大学信息科学与工程学院, 山东济南 250014

收稿日期:2014-07-01 修回日期:2014-07-09 发布日期:2014-09-24
作者简介:曲滨鹏（1975-），男，硕士，讲师，研究方向为计算机技术工程.E-mail：Teacherqu@163.com

A study of adaptive Memetic algorithm based on particle swarm optimization

QU Bin-peng¹, WANG Zhi-hao²

1. Department of Health Management, Shandong Medical College, Jinan 250002, Shandong, China;
2. School of Information Science and Engineering, Shandong Normal University, Jinan 250014, Shandong, China

Received:2014-07-01 Revised:2014-07-09 Published:2014-09-24

摘要/Abstract

摘要： 通过对已提出的适应Memetic算法的研究与分析，采用改进粒子群优化作为Memetic算法的全局优化策略按照不同类型的适应Memetic算法构成六类基于粒子群优化的适应Memetic算法，并用于求解典型的测试函数。根据对实验结果分析这几类算法的优缺点。实验结果表明适应PMemetic算法提高了全局搜索能力、收敛速度和解的精度。

关键词: Memetic算法, 局部搜索策略, 粒子群优化

Abstract: According to the analyzing of the proposed adaptive Memetic algorithm,the Particle Swarm Optimization is used as Memetic algorithm's GS. An Adaptive Memetic Algorithm Based on Particle Swarm Optimization is constituted according to different types of Adaptive Memetic. The experimental results show that the PMemetic algorithm improves the global search capability, the convergence speed and the solution accuracy. Experimental results demonstrate that the algorithm can get the better optimal path.

Key words: local search, Memetic algorithm, particle swarm optimization

中图分类号:

TP301

曲滨鹏, 王智昊. 基于粒子群优化的适应Memetic算法分析[J]. 山东大学学报（理学版）, 2014, 49(08): 118-124.

QU Bin-peng, WANG Zhi-hao. A study of adaptive Memetic algorithm based on particle swarm optimization[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(08): 118-124.

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基于粒子群优化的适应Memetic算法分析

A study of adaptive Memetic algorithm based on particle swarm optimization

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