双种群分子动理论优化算法

计算机工程与科学

双种群分子动理论优化算法

范朝冬1,任柯1,易灵芝1,肖乐意2，朱彪明1,李杰1

(1.湘潭大学信息工程学院,湖南湘潭 411105;

2.湖南大学电气与信息工程学院,湖南长沙 410082)

收稿日期:2016-05-10 修回日期:2016-10-25 出版日期:2018-04-25 发布日期:2018-04-25
基金资助:
国家自然科学基金(61572416,61573299);湖南省自然科学基金(2016JJ3125);湖南省教育厅科学研究项目(15C1327);湖南省研究生科研创新项目（XC2017B339）;湘潭大学科研项目(15XZX31,16XZX30);湘潭大学博士科研项目(11KZ|KZ08062)

A dual population based molecular

kinetic theory optimization algorithm

FAN Chaodong1,REN Ke1,YI Lingzhi1,XIAO Leyi2,ZHU Biaoming1,LI Jie1

(1.College of Information Engineering,Xiangtan University,Xiangtan 411105;

2.College of Electrical and Information Engineering,Hunan University,Changsha 410082,China)

Received:2016-05-10 Revised:2016-10-25 Online:2018-04-25 Published:2018-04-25

摘要/Abstract

摘要：

针对传统分子动理论优化算法存在寻优精度差、易陷入局部极值等不足，提出了一种双种群分子动理论优化算法。该算法将种群分为精英和普通两个子群：普通子群采用传统分子动理论优化算法搜索策略进行大范围搜索，而精英子群则通过协同合作实现精细化搜索，以提高算法收敛精度；基于个体迁移实现子群间的信息交流，两个子群通过分工合作共同完成搜索过程。实验结果表明：改进算法在收敛速度、精度和算法稳定性等方面都有明显改善。

关键词: 分子动理论优化算法, 双种群, 波动算子, 局部极值

Abstract:

The molecular kinetic theory optimization algorithm has the disadvantages of poor optimization accuracy and ease of being stuck into local extremum. A new molecular kinetic theory optimization algorithm with two populations is proposed. In this algorithm, the whole population is divided into two subgroups: the elite subgroup and the ordinary subgroup. The ordinary subgroup uses the search strategy of traditional molecular kinetic theory optimization algorithm to do a wide range search. Through cooperation, the elite subgroup does a refinement search to improve the convergence accuracy of the algorithm. Information exchange among subgroups is completed through individual migration. Two subgroups complete the search process by cooperation. Test results show that the improved algorithm significantly improves the convergence speed, accuracy and stability.

Key words: molecular kinetic theory optimization algorithm, dual population, wave operator, local extremum

范朝冬1,任柯1,易灵芝1,肖乐意2，朱彪明1,李杰1. 双种群分子动理论优化算法[J]. 计算机工程与科学.

FAN Chaodong1,REN Ke1,YI Lingzhi1,XIAO Leyi2,ZHU Biaoming1,LI Jie1.

A dual population based molecular

kinetic theory optimization algorithm

[J]. Computer Engineering & Science.

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