适应度步长的全局局部协同优化算法  

Global-Local Cooperative Optimization Algorithm with Fitness Step Size

作　　者：初雅莉 韩旭明 王晏泽 吕帅 CHU Yali;HAN Xuming;WANG Yanze;L Shuai(School of Mathematics and Statistics,Changchun University of Technology,Changchun 130012,China;College of Information Science and Technology,Jinan University,Guangzhou 510632,China;Engineering Research Center of Trustworthy AI of Ministry of Education,Guangzhou 510632,China)

机构地区：[1]长春工业大学数学与统计学院,长春130012 [2]暨南大学信息科学技术学院,广州510632 [3]可信人工智能教育部工程研究中心,广州510632

出　　处：《吉林大学学报（理学版）》2024年第6期1419-1425,共7页Journal of Jilin University:Science Edition

基　　金：国家社会科学基金(批准号:22BTJ057,24BTJ041,24CTJ034);吉林省自然科学基金(批准号:20200201164JC).

摘　　要：针对现有优化算法求解精度低的问题,提出一种适应度步长的全局局部协同优化算法.该算法通过均衡化个体适应度,动态分配每次迭代中个体的全局搜索步长和局部搜索步长,实现了算法在解空间内全局搜索和局部搜索的有效协同,进而提升了求解精度.实验结果表明,该算法在基准函数测试中展现了较高的精度和良好的稳定性,并通过仿真实验验证了其在解决复杂工程优化问题中的有效性.Aiming at the problem of low solution precision in existing optimization algorithms,we proposed a global-local cooperative optimization algorithm with fitness step size.The algorithm achieved effective collaboration between global and local search in the solution space by balancing individual fitness and dynamically allocating global and local search step sizes during each iteration,thereby enhancing the solution precision.Experimental results show that the proposed algorithm has high precision and stability in benchmark function tests,and its effectiveness in solving complex engineering optimization problems is verified through simulation experiments.

关 键 词：优化算法 均衡化适应度值 适应度步长 协同搜索 工程优化 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]