基于精英反向学习的改进BAS算法  

Improved BAS Algorithm Based on Elite Opposition-based Learning

作　　者：丁瑞成 刘学[1] 杨孟刚 郑焕祺 DING Rui-cheng;LIU Xue;YANG Meng-gang;ZHENG Huan-qi(School of Information and Electrical Engineering,Shandong Jianzhu University,Ji’nan 250101,China;Shandong Institute for Quality Inspection,Ji’nan 250102,China;School of Architecture and Urban Planning,Shandong Jianzhu University,Ji’nan 250101,China)

机构地区：[1]山东建筑大学信息与电气学院,山东济南250101 [2]山东省产品质量检验研究院,山东济南250102 [3]山东建筑大学建筑城规学院,山东济南250101

出　　处：《软件导刊》2023年第5期57-63,共7页Software Guide

基　　金：山东建筑大学博士基金项目(X21110Z)。

摘　　要：针对标准天牛须算法(BAS)收敛精度不高、容易陷入局部最优值的问题,提出一种基于精英反向学习的改进BAS算法(IBAS)。首先采用天牛种群代替单一天牛个体,通过种群的精英个体指引天牛个体的搜索路径,提高种群搜索效率;然后引入精英反向学习策略增加种群的多样性,改善算法的全局寻优能力。基于8个基准函数分别在低维与高维进行仿真实验,相较于其他比较算法,IBAS算法取得了更优的寻优精度与鲁棒性。Considering the defects of the low convergence accuracy and the weak exploration ability of standard BAS algorithm,an improved BAS algorithm(IBAS)based on elite opposition-based learning strategy is proposed.Firstly,a beetle swarm is adopted instead of the single individual,and the elite individual of the swarm are used to guide the search path to improve the search efficiency.Secondly,the elite opposi‐tion-based learning strategy is introduced to increase the diversity of the population and enhance the exploration ability of the algorithm.To ver‐ify the performance of the new algorithm,simulation experiments are conducted on 8 benchmark functions in low dimension and high dimen‐sion respectively.The experimental results indicate that the IBAS algorithm is superior to the other three algorithms in terms of optimization accuracy and robustness.

关 键 词：天牛须算法 精英个体 反向学习策略 全局寻优 基准函数 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]