基于动态参数的人工搜索群算法  

Artificial search swarm algorithm based on dynamic parameters

作　　者：陈堂功[1,2] 刘超[1,2] 王梦莹 周小婷 黄涛 CHEN Tang-gong;LIU Chao;WANG Meng-ying;ZHOU Xiao-ting;HUANG Tao(State Key Laboratory of Reliability and Intelligence of Eletrical Equipment,Hebei University of Technology, Tianjin 300130,China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province,Hebei University of Technology,Tianjin 300130,China)

机构地区：[1]河北工业大学省部共建电工装备可靠性与智能化国家重点实验室,天津300130 [2]河北工业大学河北省电磁场与电器可靠性重点实验室,天津300130

出　　处：《控制与决策》2019年第9期1923-1928,共6页Control and Decision

摘　　要：人工搜索群算法(Artificial search swarm algorithm, ASSA)是受人类士兵通过信息交流完成目标搜索行为及过程启发而设计的一种仿生智能算法.针对基本人工搜索群算法中固定参数可能导致算法过早陷入局部最优解的缺陷,提出一种动态参数改进人工搜索群算法(Improved artificial search swarm algorithm, IASSA).通过引入全局权重系数改善侦查行为中搜索群的历史经验位置,从而加强全局最优个体对整个种群的引导作用;利用动态协同参数提高士兵协同行为的搜索概率,以加强种群之间局部信息交流;采用动态步长策略提高算法的收敛速度和收敛精度;为了检验改进算法的优化性能,采用15个测试函数进行仿真实验.实验结果表明,所提出的改进算法可有效避免早熟现象,在收敛速度和收敛精度上较基本人工搜索群算法和若干同类优化算法有显著提高.The artificial search swarm algorithm(ASSA) is a kind of bionic intelligent algorithm, which is inspired by the human communicating with each other in search. The fixed collaborative parameter and global weight in the standard artificial search swarm algorithm easily lead to local convergence and influence the convergence speed. Therefore, this paper proposes an improved artificial search swarm algorithm(IASSA) by introducing global weight, linearly dependent synergistic coefficient and dynamic step. The global weight improves the historical experience of the search group in reconnaissance behavior, so as to strengthen the guiding role of global optimal individual for all population. The dynamic collaborative parameters are used to improve the search probability of the soldier’s cooperative behavior, so as to enhance the local information exchange between the populations. The dynamic step strategy is used to improve the convergence speed and convergence accuracy. The performance of the algorithm is verified on 15 benchmark functions. The results show that the improved algorithm can effectively avoid the local convergence and significantly improve the solution accuracy and convergence speed compared with the standard ASSA algorithm and the other three kinds of optimization algorithms.

关 键 词：仿生智能算法 人工搜索群算法 全局权重系数 动态协同参数 动态步长 函数优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]