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

摘要： 侏儒猫鼬优化算法（dwarf mongoose optimization，DMO）是新提出的一种元启发式算法，该算法具有较强的全局探索能力和稳定性，但由于原始算法中仅依靠雌性首领带领整个猫鼬种群进行搜索，会产生收敛速度较慢、易陷入局部最优以及探索阶段与开发阶段之间的平衡较差等问题。针对上述问题，提出一种融合透镜成像反向学习的精英池侏儒猫鼬优化算法（improved dwarf mongoose optimization，IDMO），采用透镜成像反向学习策略，避免算法在迭代过程中陷入局部最优，增强算法的探索能力；在阿尔法组觅食时引入精英池策略，提高了算法的收敛精度，进一步增强算法探索能力。通过基准测试函数进行实验，表明IDMO算法具有良好的寻优性能和鲁棒性，且算法收敛速度得到显著提升。通过对汽车碰撞优化问题的求解，进一步验证了IDMO算法具有良好的适用性和有效性。

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