A Global Best-guided Firefly Algorithm for Engineering Problems

作     者：Mohsen Zare Mojtaba Ghasemi Amir Zahedi Keyvan Golalipour Soleiman Kadkhoda Mohammadi Seyedali Mirjalili Laith Abualigah Mohsen Zare;Mojtaba Ghasemi;Amir Zahedi;Keyvan Golalipour;Soleiman Kadkhoda Mohammadi;Seyedali Mirjalili;Laith Abualigah

作者机构：Department of Electrical EngineeringFaculty of EngineeringJahrom UniversityJahrom***FrasIran Department of Electronics and Electrical EngineeringShiraz University of TechnologyShiraz***Iran Department of Electrical and Computer EngineeringTarbiat Modares UniversityTehran***Iran Department of Computer EngineeringSari BranchIslamic Azad UniversitySari***Iran Department of Electrical EngineeringUrmia BranchIslamic Azad UniversityUrmia571696896Iran Centre for Artificial Intelligence Research and OptimisationTorrens University AustraliaBrisbaneQLD4006Australia Yonsei Frontier LabYonsei UniversitySeoul03722South Korea Computer Science DepartmentPrince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific ResearchAl-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information TechnologyMiddle East UniversityAmman11831Jordan School of Computer SciencesUniversiti Sains Malaysia11800George TownPulau PinangMalaysia University Research and Innovation CenterObuda University1034BudapestHungary School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Applied science research centerApplied science private universityAmman11931Jordan 

出 版 物：《Journal of Bionic Engineering》 (仿生工程学报（英文版）)

年 卷 期：2023年第20卷第5期

页      面：2359-2388页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：Firefly algorithm New movement vector Global best-guided firefly algorithm Global optimization Engineering design 

摘      要：The Firefly Algorithm(FA)is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when *** article proposes a method based on Differential Evolution(DE)/current-to-best/1 for enhancing the FA s movement *** proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best ***,employing the best solution can lead to premature algorithm convergence,but this study handles this issue using a loop adjacent to the algorithm s main ***,the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original *** GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha ***,the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced *** all cases,GbFA provides the optimal result compared to other *** that the source code of the GbFA algorithm is publicly available at https://***/projects/gbfa.