Multi-objective particle swarm optimization algorithm based on global best position adaptive selection and local search

doi:10.11772/j.issn.1001-9081.2014.04.1074

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (4): 1074-1079.DOI: 10.11772/j.issn.1001-9081.2014.04.1074

• Artificial intelligence • Previous Articles Next Articles

Multi-objective particle swarm optimization algorithm based on global best position adaptive selection and local search

HUANG Min¹,JIANG Yu¹,MAO An²,JIANG Qi¹

1. State Key Laboratory of Power Transmission Equipment and System Security and New Technology (Chongqing University), Chongqing 400044, China
2. College of Materials Science and Engineering, Chongqing University, Chongqing 400044, China

Received:2013-09-17 Revised:2013-11-15 Online:2014-04-29 Published:2014-04-01
Contact: HUANG Min

基于全局最优位置自适应选取与局部搜索的多目标粒子群优化算法

黄敏¹,江渝¹,毛安²,姜琪¹

1. 输配电装备及系统安全与新技术国家重点实验室(重庆大学)，重庆 400044；
2. 重庆大学材料科学与工程学院，重庆 400044

通讯作者: 黄敏
作者简介:黄敏(1990-),女,江西吉安人,硕士研究生，主要研究方向:智能优化算法、微电网运行优化；
江渝(1964-),男,重庆人,副教授，博士，主要研究方向:电力电子、电力系统；
毛安(1989-),男,江西吉安人,硕士研究生，主要研究方向:智能算法；
姜琪(1989-),男,江苏常州人,硕士研究生，主要研究方向:电力电子、电力系统。
基金资助:
国家自然科学基金资助项目

Abstract

Abstract:

To deal with the problems of the strategies for selecting the global best position and the low local search ability, a multi-objective particle swarm optimization algorithm based on global best position adaptive selection and local search named MOPSO-GL was proposed. During the guiding particles selection in MOPSO-GL, the Sigma method and crowding distance of the particle in the archive were used and the archive member chose the guided particles in the swarm to improve the solution diversity and the swarm uniformity. Therefore, the population might get close to the true Pareto optimal solutions uniformly and quickly. Furthermore, the improved chaotic optimization strategy based on Skew Tent map was adopted, to improve the local search ability and the convergence of MOPSO-GL when the search ability of MOPSO-GL got weak. The simulation results show that MOPSO-GL has better convergence and distribution.

摘要：

针对多目标粒子群优化算法全局最优位置〖BP(〗(gbest)〖BP)〗选取存在的缺陷和局部搜索能力弱的缺点，提出一种基于全局最优位置自适应选取与局部搜索的多目标粒子群优化算法MOPSO-GL。首先对Sigma法进行改进，引入拥挤距离机制，不再是粒子从档案中选择全局最优位置，而是档案成员从种群中选择合适的被引导粒子，引导种群均匀快速地向Pareto前沿飞行，提高了Pareto解的收敛性和多样性；其次当种群寻优能力减弱时，引入基于Skew Tent映射的变尺度全面搜索混沌优化策略对外部档案进行局部搜索，以提高算法的收敛性；最后通过与其他多目标优化算法的比较，结果表明MOPSO-GL具有更好的收敛性和分布性。

CLC Number:

TP18
TP301.6

HUANG Min JIANG Yu MAO An JIANG Qi. Multi-objective particle swarm optimization algorithm based on global best position adaptive selection and local search[J]. Journal of Computer Applications, 2014, 34(4): 1074-1079.

黄敏江渝毛安姜琪. 基于全局最优位置自适应选取与局部搜索的多目标粒子群优化算法[J]. 计算机应用, 2014, 34(4): 1074-1079.

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Multi-objective particle swarm optimization algorithm based on global best position adaptive selection and local search

基于全局最优位置自适应选取与局部搜索的多目标粒子群优化算法

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