Salp Swarm Incorporated Adaptive Dwarf Mongoose Optimizer with Lévy Flight and Gbest-Guided Strategy

作     者：Gang Hu Yuxuan Guo Guanglei Sheng Gang Hu;Yuxuan Guo;Guanglei Sheng

作者机构：Department of Applied MathematicsXi’an University of TechnologyXi’an710054People’s Republic of China School of Computer Science and EngineeringXi’an University of TechnologyXi’an710048People’s Republic of China Department of Electronics and Information EngineeringBozhou UniversityBozhou236800People’s Republic of China 

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

年 卷 期：2024年第21卷第4期

页      面：2110-2144页

核心收录：

学科分类：08[工学] 09[农学] 0901[农学-作物学] 0836[工学-生物工程] 090102[农学-作物遗传育种] 

基　　金：National Natural Science Foundation of China Grant No.52375264 

主　　题：Dwarf mongoose optimization algorithm Gbest-guided Lévy flight Adaptive parameter Salp swarm algorithm Engineering optimization Truss topological optimization 

摘      要：In response to the shortcomings of Dwarf Mongoose Optimization(DMO)algorithm,such as insufficient exploitation capability and slow convergence speed,this paper proposes a multi-strategy enhanced DMO,referred to as ***,we propose an improved solution search equation that utilizes the Gbest-guided strategy with different parameters to achieve a trade-off between exploration and exploitation(EE).Secondly,the Lévy flight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local *** addition,in order to address the problem of low convergence efficiency of DMO,this study uses the strong nonlinear convergence factor Sigmaid function as the moving step size parameter of the mongoose during collective activities,and combines the strategy of the salp swarm leader with the mongoose for cooperative optimization,which enhances the search efficiency of agents and accelerating the convergence of the algorithm to the global optimal solution(Gbest).Subsequently,the superiority of GLSDMO is verified on CEC2017 and CEC2019,and the optimization effect of GLSDMO is analyzed in *** results show that GLSDMO is significantly superior to the compared algorithms in solution quality,robustness and global convergence rate on most test ***,the optimization performance of GLSDMO is verified on three classic engineering examples and one truss topology optimization *** simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.