An Improved Gorilla Troops Optimizer Based on Lens Opposition-Based Learning and Adaptive β-Hill Climbing for Global Optimization  

作　　者：Yaning Xiao Xue Sun Yanling Guo Sanping Li Yapeng Zhang Yangwei Wang 

机构地区：[1]College of Mechanical and Electrical Engineering,Northeast Forestry University,Harbin,150040,China

出　　处：《Computer Modeling in Engineering & Sciences》2022年第5期815-850,共36页工程与科学中的计算机建模（英文）

基　　金：This work is financially supported by the Fundamental Research Funds for the Central Universities under Grant 2572014BB06.

摘　　要：Gorilla troops optimizer(GTO)is a newly developed meta-heuristic algorithm,which is inspired by the collective lifestyle and social intelligence of gorillas.Similar to othermetaheuristics,the convergence accuracy and stability of GTOwill deterioratewhen the optimization problems to be solved becomemore complex and flexible.To overcome these defects and achieve better performance,this paper proposes an improved gorilla troops optimizer(IGTO).First,Circle chaotic mapping is introduced to initialize the positions of gorillas,which facilitates the population diversity and establishes a good foundation for global search.Then,in order to avoid getting trapped in the local optimum,the lens opposition-based learning mechanism is adopted to expand the search ranges.Besides,a novel local search-based algorithm,namely adaptiveβ-hill climbing,is amalgamated with GTO to increase the final solution precision.Attributed to three improvements,the exploration and exploitation capabilities of the basic GTOare greatly enhanced.The performance of the proposed algorithm is comprehensively evaluated and analyzed on 19 classical benchmark functions.The numerical and statistical results demonstrate that IGTO can provide better solution quality,local optimumavoidance,and robustness compared with the basic GTOand five other wellknown algorithms.Moreover,the applicability of IGTOis further proved through resolving four engineering design problems and training multilayer perceptron.The experimental results suggest that IGTO exhibits remarkable competitive performance and promising prospects in real-world tasks.

关 键 词：Gorilla troops optimizer circle chaotic mapping lens opposition-based learning adaptiveβ-hill climbing 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]