Artificial rabbit optimization algorithm based on chaotic mapping and Levy flight improvement

作     者：Wu Jin Su Zhengdong Gao Yaqiong Feng Haoran Wu Jin;Su Zhengdong;Gao Yaqiong;Feng Haoran

作者机构：School of Electronic EngineeringXi’an University of Posts and TelecommunicationsXi’an 710121China 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报（英文版）)

年 卷 期：2024年第31卷第4期

页      面：54-69页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Key R&D Program of China:Science and Technology Innovation 2030(2022ZD0119001) 

主　　题：CLARO chaotic mapping Levy flight convergence 

摘      要：An artificial rabbit optimization(ARO)algorithm based on chaotic mapping and Levy flight improvement is proposed,which has the advantages of good initial population quality and fast convergence compared with the traditional ARO algorithm,called *** is improved by applying three *** mapping is introduced,which can optimize the quality of the initial population of the *** Levy flight in the exploration phase,which can avoid the algorithm from falling into a local *** threshold of the energy factor is optimized,which can better balance exploration and *** efficiency of CLARO is tested on a set of 23 benchmark function sets by comparing it with ARO and different meta-heuristics *** last,the comparison experiments conclude that all three improvement strategies enhance the performance of ARO to some extent,with Levy flight providing the most significant improvement in ARO *** experimental results show that CLARO has better results and faster convergence compared to other algorithms,while successfully addressing the drawbacks of ARO and being able to face more challenging problems.