关键词: 蜣螂优化器, 蝴蝶优化算法, 螺旋偷窃, 混合变异, 工程问题

Abstract: Improved dung beetle optimizer is proposed to solve the disadvantages of the dung beetle optimizer such as weak stability, and sudden decrease in population diversity during the later iteration. Firstly, by using the good point set strategy to generate the population, the distribution of population positions becomes more balanced, laying the foundation for global optimization. Besides, the butterfly optimization algorithm with random inertia weight is integrated to enhance the ability of population mutation, increase the exploration degree of dung beetles in unknown fields, and balance global exploration and local development. Then, the stealing beetle endowed with the ability of adaptive spiral theft helps the algorithm conduct more detailed search. Finally, the lens imaging learning strategy , the throw out cross strategy and the greedy strategy are used to improve the algorithm’s ability to resist stagnation and help the algorithm capture global optima. Through comparative experiments on 15 benchmark test functions and partial CEC2014 test functions, ablation experiments with different strategies, Wilcoxon test, it is shown that the new algorithm has stronger robustness. The effectiveness of the improvement strategy and the engineering practicality of the algorithm are further verified by applying the improved algorithm to tension/compression spring engineering problem, three bar truss engineering problem, bearing fault diagnosis optimization problem.

Key words: dung beetle optimizer, butterfly optimization algorithm, spiral theft, hybrid mutation, engineering problem