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

Abstract

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 GLSDMO. Firstly, 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 optimum. In 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 detail. The results show that GLSDMO is significantly superior to the compared algorithms in solution quality, robustness and global convergence rate on most test functions. Finally, the optimization performance of GLSDMO is verified 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.