基于K-Means全局引导策略的多目标微粒群算法

doi:10.13190/jbupt.201205.49.253

北京邮电大学学报 ›› 2012, Vol. 35 ›› Issue (5): 49-53.doi: 10.13190/jbupt.201205.49.253

基于K-Means全局引导策略的多目标微粒群算法

仇晨晔, 王春露, 左兴权, 方滨兴

可信分布式计算与服务教育部重点实验室(北京邮电大学)

收稿日期:2011-10-31 修回日期:2012-04-26 出版日期:2012-10-28 发布日期:2012-07-06
通讯作者: 仇晨晔 E-mail:qiuchenye@gmail.com
作者简介:仇晨晔（1986-），男，博士生，E-mail：qiuchenye@gmai.com 方滨兴（1955-），男，教授，博士生导师
基金资助:
国家高技术研究发展计划（2009AA04Z120）；中国高校基本科研业务费项目（2009RC0208）

Multi-Objective Particle Swarm Optimization Based on a K-Means Guide Selection Strategy

QIU Chen-ye, WANG Chun-lu, ZUO Xing-quan, FANG Bin-xing

Key Laboratory of Trustworthy Distributed Computing and Service (Beijing University of Posts and Telecommunications)

Received:2011-10-31 Revised:2012-04-26 Online:2012-10-28 Published:2012-07-06

摘要/Abstract

摘要：

提出了一种基于K-means全局引导策略的多目标微粒群算法(KMOPSO),通过K-means算法从归档集中选出K个均匀分布的非支配粒子作为全局最优引导,以保证种群中的粒子向整个Pareto前端移动,提高解的多样性. 用基于最近邻居的剪枝算法控制归档集规模,同时保证其中非支配解的多样性. 引入变异策略来加强算法的局部搜索能力,避免早熟收敛. 用5个经典函数进行了仿真测试,实验结果表明,该算法能有效地解决多目标优化问题,不但能收敛于Pareto最优前端,而且在解的多样性方面优于改进的非劣分类遗传算法和基于拥挤距离的多目标微粒群算法.

关键词: 微粒群算法, 多目标优化, K-means算法

Abstract:

Multi-objective particle swarm optimization based on a K-means guide selection strategy (KMOPSO) is proposed. A K-means algorithm based guide selection strategy is used to select K evenly located non-dominated particles from the archive in order to ensure the particles in the population move to the entire Pareto front and improve the diversity of solutions. A pruning method based on the nearest neighbour is adopted to control the size of the archive, while preserving the diversity of the archive. A mutation operator is presented to improve the exploration ability for preventing from premature. Simulation on five classical test functions indicates the feasibility of the proposed algorithm. KMOPSO can generate non-dominated solutions close to the true Pareto front and outperform non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization with crowding distance in terms of the diversity of non-dominated solutions.

Key words: particle swarm optimization, multi-objective optimization, K-means algorithm

中图分类号:

TP18

仇晨晔, 王春露, 左兴权, 方滨兴. 基于K-Means全局引导策略的多目标微粒群算法[J]. 北京邮电大学学报, 2012, 35(5): 49-53.

QIU Chen-ye, WANG Chun-lu, ZUO Xing-quan, FANG Bin-xing. Multi-Objective Particle Swarm Optimization Based on a K-Means Guide Selection Strategy[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2012, 35(5): 49-53.

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基于K-Means全局引导策略的多目标微粒群算法

Multi-Objective Particle Swarm Optimization Based on a K-Means Guide Selection Strategy

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