一种以优秀个体记忆位置为导向的改进乌鸦搜索算法  

Improved Crow Search Algorithm Based on Excellent Individual Memory Location

作　　者：张宁 王勇[1,2] 张伟 ZHANG Ning;WANG Yong;ZHANG Wei(College of Artificial Intelligence,Guangxi Minzu University,Nanning 530006,China;Guangxi Key Laboratory of Hybrid Computation&IC Design Analysis,Nanning 530006,China)

机构地区：[1]广西民族大学人工智能学院,南宁530006 [2]广西混杂计算与集成电路设计分析重点实验室,南宁530006

出　　处：《小型微型计算机系统》2024年第5期1089-1098,共10页Journal of Chinese Computer Systems

基　　金：国家自然科学基金资助项目(62266007)资助;广西自然科学基金资助项目(2021GXNSFAA220068)资助。

摘　　要：为了克服乌鸦搜索算法搜索能力弱、易陷入局部最优之不足,提出新的以优秀个体记忆位置为导向的改进乌鸦搜索算法(EICSA):基于个体贮藏食物量之多少,种群中多数个体划归为普通个体、少数贮藏食物量较多的个体划归为优秀个体.优秀个体只在其贮藏食物的巢穴附近开展局部搜索活动.多数普通个体以优秀个体贮藏食物之巢穴为导向,在算法前期以较大步长进行全局探索,保持了种群的多样性;算法后期则以较短步长进行局部开发,使算法的全局探索能力和局部开发能力均得到了增强.通过12个基准函数和3个工程应用问题的数值实验,结果表明EICSA的全局优化能力得到了明显提高,在函数和工程应用问题优化中具有较快的全局收敛速度、较好的优化精度和稳定性.In order to overcome the shortcomings of crow search algorithm,including weak search ability and easy to fall into local optimum,a new improved Crow Search Algorithm(EICSA)based on the memory location of excellent individuals is proposed:Based on the amount of food stored by individuals,most individuals in the population are classified as ordinary individuals,and a few individuals with more food stored are classified as excellent individuals.Each excellent individual conducts search activities only in the vicinity of the nest where its food is stored.Under the guidance of excellent individuals′food storage nests,most ordinary individuals conduct global exploration in the search space with large steps and maintain the diversity of the population in the early stage of the algorithm,and carry out local exploitation with short steps in the later stage of the algorithm,which makes both the global exploration ability and the local exploitation ability of the algorithm enhanced.Through the numerical experiments of 12 benchmark functions and 3 engineering application problems,the results show that the global optimization ability of EICSA has been significantly improved,and it has faster global convergence speed,better optimization accuracy and stability in the optimization of functions and engineering application problems.

关 键 词：乌鸦搜索算法(CSA) 智能优化 优秀个体 普通个体 工程约束优化问题 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]