Modified crayfish optimization algorithm with adaptive spiral elite greedy opposition-based learning and search-hide strategy for global optimization

作     者：Li, Guanghui Zhang, Taihua Tsai, Chieh-Yuan Lu, Yao Yang, Jun Yao, Liguo 

作者机构：Guizhou Normal Univ Sch Mech & Elect Engn Dept Mech Engn Guiyang 550025 Guizhou Peoples R China Guizhou Normal Univ Tech Engn Ctr Mfg Serv & Knowledge Engn Guiyang 550025 Guizhou Peoples R China Yuan Ze Univ Dept Ind Engn & Management Taoyuan 32003 Taiwan 

出 版 物：《JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING》 (J. Comput. Des. Eng.)

年 卷 期：2024年第11卷第4期

页      面：249-305页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Guizhou Provincial Basic Research Program (Natural Science) [Qiankehejichu-ZKGeneral 320, Qiankehejichu-ZKGeneral 424] National Natural Science Foundation Natural Science Research Project of Guizhou Provincial Education Department (Youth Science and Technology Talent Development Project) [Qianjiaoji 42] Academic New Seedling Foundation Project of Guizhou Normal University [Qianshixinmiao-A30] 

主　　题：crayfish optimization algorithm search-hide opposition-based learning competition-elimination chaos mutation engineering optimization 

摘      要：Crayfish optimization algorithm (COA) is a novel bionic metaheuristic algorithm with high convergence speed and solution accuracy. However, in some complex optimization problems and real application scenarios, the performance of COA is not satisfactory. In order to overcome the challenges encountered by COA, such as being stuck in the local optimal and insufficient search range, this paper proposes four improvement strategies: search-hide, adaptive spiral elite greedy opposition-based learning, competition-elimination, and chaos mutation. To evaluate the convergence accuracy, speed, and robustness of the modified crayfish optimization algorithm (MCOA), some simulation comparison experiments of 10 algorithms are conducted. Experimental results show that the MCOA achieved the minor Friedman test value in 23 test functions, CEC2014 and CEC2020, and achieved average superiority rates of 80.97%, 72.59%, and 71.11% in the WT, respectively. In addition, MCOA shows high applicability and progressiveness in five engineering problems in actual industrial field. Moreover, MCOA achieved 80% and 100% superiority rate against COA on CEC2020 and the fixed-dimension function of 23 benchmark test functions. Finally, MCOA owns better convergence and population diversity. Graphical Abstract