关键词: 侏儒猫鼬优化算法, 元启发式算法, 透镜成像反向学习策略, 精英池策略

Abstract: Dwarf mongoose optimization（DMO） is a newly proposed meta heuristic algorithm. The algorithm has strong global exploration ability and stability. However, due to the fact that only female leader is used in the original algorithm to lead the whole mongoose population to search, there will be some problems, such as slow convergence speed, easy to fall into local optimization and poor balance between exploration stage and exploitation stage. To solve the above problems, this paper proposes an improved dwarf mongoose optimization（IDMO）. Firstly, the lens imaging reverse learning strategy is adopted to avoid the algorithm falling into local optimization in the iterative process and enhance the exploration ability of the algorithm. Then the elite pool strategy is introduced into the Alpha group foraging, which improves the convergence accuracy of the algorithm and further enhances the exploration ability of the algorithm. Experiments with benchmark function show that IDMO has good optimization performance and robustness, and the convergence speed of the algorithm is significantly improved. Finally, by solving the car crash worthiness optimization problem, it is further verifies that the IDMO algorithm has good applicability and effectiveness.

Key words: dwarf mongoose optimization algorithm, meta heuristic algorithm, lens imaging reverse learning strategy, elite pool strategy