Wild Gibbon Optimization Algorithm  

作　　者：Jia Guo JinWang Ke Yan Qiankun Zuo Ruiheng Li Zhou He Dong Wang Yuji Sato 

机构地区：[1]Hubei Key Laboratory of Digital Finance Innovation,Hubei University of Economics,Wuhan,430205,China [2]School of Information Engineering,Hubei University of Economics,Wuhan,430205,China [3]China Construction Third Engineering Bureau Installation Engineering Co.,Ltd.,Wuhan,430074,China [4]Hubei Internet Finance Information Engineering Technology Research Center,Hubei University of Economics,Wuhan,430205,China [5]College of Informatics,Huazhong Agricultural University,Wuhan,430070,China [6]Faculty of Computer and Information Sciences,Hosei University,Tokyo,184-8584,Japan

出　　处：《Computers, Materials & Continua》2024年第7期1203-1233,共31页计算机、材料和连续体（英文）

基　　金：funded by Natural Science Foundation of Hubei Province Grant Numbers 2023AFB003,2023AFB004;Education Department Scientific Research Program Project of Hubei Province of China Grant Number Q20222208;Natural Science Foundation of Hubei Province of China(No.2022CFB076);Artificial Intelligence Innovation Project of Wuhan Science and Technology Bureau(No.2023010402040016);JSPS KAKENHI Grant Number JP22K12185.

摘　　要：Complex optimization problems hold broad significance across numerous fields and applications.However,as the dimensionality of such problems increases,issues like the curse of dimensionality and local optima trapping also arise.To address these challenges,this paper proposes a novel Wild Gibbon Optimization Algorithm(WGOA)based on an analysis of wild gibbon population behavior.WGOAcomprises two strategies:community search and community competition.The community search strategy facilitates information exchange between two gibbon families,generating multiple candidate solutions to enhance algorithm diversity.Meanwhile,the community competition strategy reselects leaders for the population after each iteration,thus enhancing algorithm precision.To assess the algorithm’s performance,CEC2017 and CEC2022 are chosen as test functions.In the CEC2017 test suite,WGOA secures first place in 10 functions.In the CEC2022 benchmark functions,WGOA obtained the first rank in 5 functions.The ultimate experimental findings demonstrate that theWildGibbonOptimization Algorithm outperforms others in tested functions.This underscores the strong robustness and stability of the gibbonalgorithm in tackling complex single-objective optimization problems.

关 键 词：Complex optimization wild gibbon optimization algorithm community search community competition 

分 类 号：O17[理学—数学]