BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems  

作　　者：Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja  Kherchouche 

机构地区：[1]Department of Computer Science and Information Technology,Kasdi Merbah University,P.O.Box 30000,Ouargla,Algeria [2]Department of Computer Science,College of Computing and Informatics,University of Sharjah,P.O.Box 27272,Sharjah,United Arab Emirates [3]Department of Computer Engineering,College of Computer and Information Sciences,King Saud University,P.O.Box 51178,Riyadh,11543,Saudi Arabia [4]Operations Research Department,Faculty of Graduate Studies for Statistical Research,Cairo University,Giza,12613,Egypt [5]Applied Science Research Center,Applied Science Private University,Amman,11937,Jordan [6]Guizhou Key Laboratory of Intelligent Technology in Power System,College of Electrical Engineering,Guizhou University,Guiyang,550025,China

出　　处：《Computer Modeling in Engineering & Sciences》2024年第10期219-265,共47页工程与科学中的计算机建模（英文）

基　　金：funded by the Researchers Supporting Program at King Saud University(RSPD2024R809).

摘　　要：Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability i

关 键 词：Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]