引用本文:吴亚丽,付玉龙,王鑫睿,刘庆.目标空间聚类的差分头脑风暴优化算法[J].控制理论与应用,2017,34(12):1583~1593.[点击复制]
WU ya-li,FU Yu-long,WANG Xin-rui,LIU Qin.Difference brain storm optimization algorithm based on clustering in objective space[J].Control Theory and Technology,2017,34(12):1583~1593.[点击复制]
目标空间聚类的差分头脑风暴优化算法
Difference brain storm optimization algorithm based on clustering in objective space
摘要点击 3442  全文点击 1903  投稿时间：2016-11-29  修订日期：2017-09-05
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DOI编号  10.7641/CTA.2017.60905
  2017,34(12):1583-1593
中文关键词  头脑风暴算法  聚类  差分变异  目标空间
英文关键词  brain storm optimization (BSO)  cluster  difference mutation  objective space
基金项目  国家自然科学基金青年基金项目(61503299, 61502385)
作者单位E-mail
吴亚丽* 西安理工大学 yliwu@xaut.edu.cn 
付玉龙 西安理工大学自动化与信息工程学院  
王鑫睿 西安理工大学自动化与信息工程学院  
刘庆 西安理工大学自动化与信息工程学院  
中文摘要
      作为一种新型的群体智能优化算法, 头脑风暴优化(brain storm optimization, BSO)算法一经提出便引起了 众多研究者的关注. 本文在对原始头脑风暴算法的聚类操作和变异操作改进的基础上, 提出了基于目标空间聚类 的差分头脑风暴(difference brain storm optimization based on clustering in objective space, DBSO–OS)算法. 算法通过 对目标空间的聚类替代对决策空间的聚类, 减小了算法的运算复杂度; 采用差分变异代替高斯变异来增加种群的 多样性. 多个测试函数的仿真结果表明, 目标空间聚类的差分头脑风暴算法不仅提高了算法的寻优速度, 而且提高 了算法的寻优精度. 文中进一步分析了参数对算法性能的影响, 设计了最佳参数选择方案, 并用于对实际热电联供 经济调度问题的求解, 验证了算法的实用性.
英文摘要
      As a new kind of swarm intelligence optimization algorithm, brain storm optimization (BSO) has paid more attention of more researchers in different fields. Based on the cluster operation and mutation of original BSO, a novel BSO algorithm named difference brain storm optimization based on clustering in objective space (DBSO–OS) is proposed in this paper to improve the performance of the original BSO algorithm. The clustering operation is designed in objective space which can decrease the computation complexity comparing with clustering in decision space in the proposed algorithm. The difference mutation operation is adopted to increase the diversity of the population. The simulation results of many benchmark functions of different dimensions demonstrate that the proposed algorithm can not only improve the time performance but also the precision. Moreover, the suitable parameter selection strategy is provided on the basis of the parameter analysis of the proposed algorithm. And the combined heat and power economic dispatch (CHPED) are implemented to evaluate the effectiveness of the proposed algorithm.