Journal of Bionic Engineering ›› 2024, Vol. 21 ›› Issue (4): 2110-2144.doi: 10.1007/s42235-024-00545-z
• • 上一篇
Salp Swarm Incorporated Adaptive Dwarf Mongoose Optimizer with Lévy Flight and Gbest‑Guided Strategy
Gang Hu1,2; Yuxuan Guo1 ; Guanglei Sheng2,3
- 1 Department of Applied Mathematics, Xi’an University of Technology, Xi’an 710054, People’s Republic of China 2 School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, People’s Republic of China 3 Department of Electronics and Information Engineering, Bozhou University, Bozhou 236800, People’s Republic of China
Salp Swarm Incorporated Adaptive Dwarf Mongoose Optimizer with Lévy Flight and Gbest‑Guided Strategy
Gang Hu1,2; Yuxuan Guo1 ; Guanglei Sheng2,3
- 1 Department of Applied Mathematics, Xi’an University of Technology, Xi’an 710054, People’s Republic of China 2 School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, People’s Republic of China 3 Department of Electronics and Information Engineering, Bozhou University, Bozhou 236800, People’s Republic of China
摘要： In response to the shortcomings of Dwarf Mongoose Optimization (DMO) algorithm, such as insufcient exploitation capability and slow convergence speed, this paper proposes a multi-strategy enhanced DMO, referred to as GLSDMO. Firstly, we propose an improved solution search equation that utilizes the Gbest-guided strategy with diferent parameters to achieve a trade-of between exploration and exploitation (EE). Secondly, the Lévy fight is introduced to increase the diversity of population distribution and avoid the algorithm getting stuck in a local optimum. In addition, in order to address the problem of low convergence efciency 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 efciency of agents and accelerating the convergence of the algorithm to the global optimal solution (Gbest). Subsequently, the superiority of GLSDMO is verifed on CEC2017 and CEC2019, and the optimization efect of GLSDMO is analyzed in detail. The results show that GLSDMO is signifcantly superior to the compared algorithms in solution quality, robustness and global convergence rate on most test functions. Finally, the optimization performance of GLSDMO is verifed on three classic engineering examples and one truss topology optimization example. The simulation results show that GLSDMO achieves optimal costs on these real-world engineering problems.