基于信息共享搜索策略的自适应灰狼算法研究  

Research on Adaptive Grey Wolf Algorithm Based on Information Sharing Search Strategy

作　　者：吴昌友[1] 付熙松 裴均珂 WU Changyou;FU Xisong;PEI Junke(College of Management and Science,Shandong Institute o£Business and Technology,Yantai 264000,China)

机构地区：[1]山东工商学院管理科学与工程学院,山东烟台264000

出　　处：《电光与控制》2022年第7期22-28,共7页Electronics Optics & Control

基　　金：国家自然科学基金(41601593);山东省社科项目(13DGLJ05);山东省软科学项目(2014RKB01021)。

摘　　要：针对基础灰狼优化(GWO)算法种群多样性不足和易陷入局部最优的缺点,从混沌初始化和种群间信息共享两个角度,提出一种基于信息共享搜索策略的改进灰狼优化(ISIAGWO)算法。首先,使用Iterative混沌映射初始化种群保证种群的多样性,并引入自适应动态算子增加优秀个体权重;其次,使用信息共享搜索策略更新种群有效避免算法陷入局部最优;再次,对8种基准函数进行寻优测试并与其他先进群智能算法进行对比,实验结果表明,ISIAGWO算法在解的精度和鲁棒性上有显著提升;最后,将ISIAGWO算法应用于经典的旅行商问题进行求解,证实该算法的实用性。Aiming at the shortcomings of the basic Grey Wolf Optimization(GWO)algorithmsuch as insufficient population diversity and easy to fall into local optimuman improved grey wolf optimization(ISIAGWO)algorithm based on information sharing search strategy is proposed from the perspectives of chaos initialization and information sharing among populations.Firstlythe Iterative chaotic mapping is used to initialize the population to ensure the diversityand the adaptive dynamic operator is introduced to increase the weight of outstanding individuals;Secondlythe information sharing search strategy is used to update the population to effectively avoid the algorithm falling into local optimum;Thirdlyeight benchmark functions are tested for optimization and the proposed algorithm is compared with other advanced swarm intelligence algorithms.The experimental results show that ISIAGWO algorithm has significantly improved the accuracy and robustness of the solution;FinallyISIAGWO algorithm is applied to solve the classic traveling salesman problemso as to prove the practicability of the algorithm.

关 键 词：灰狼优化算法 信息共享搜索策略 自适应算子 Iterative映射 旅行商问题 

分 类 号：TP23[自动化与计算机技术—检测技术与自动化装置]