双种群分子动理论优化算法  

A dual population based molecular kinetic theory optimization algorithm

作　　者：范朝冬[1] 任柯[1] 易灵芝[1] 肖乐意 朱彪明 李杰[1] FAN Chao-dong;REN Ke;YI Ling-zhi;XIAO Le-yi;ZHU Biao-ming;LI Jie(College of Information Engineering,Xiangtan University,Xiangtan 411105;College of Electrical and Information Engineering,Hunan University,Changsha 410082,China)

机构地区：[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

出　　处：《计算机工程与科学》2018年第4期723-730,共8页Computer Engineering & Science

基　　金：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)

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。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.

关 键 词：分子动理论优化算法 双种群 波动算子 局部极值 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]