关键词: 蜣螂优化算法, 限制反向学习, 柯西-高斯变异, Wilcoxon秩和检验, 机器人夹持器问题

Abstract:

To overcome the shortcomings of slow convergence， low accuracy and being easy to fall into local optimum in Dung Beetle Optimizer （DBO） algorithm， a Dung Beetle Optimizer algorithm with restricted reverse learning and Cauchy-Gauss variation （SI-DBO） was proposed. Firstly， Circle mapping was used to initialize the population to make distribution of the population more uniform and diverse， which improved the convergence speed and optimization accuracy of the algorithm. Secondly， restricted reverse learning was used to update the locations of dung beetles， so as to improve the search ability of dung beetles. Finally， Cauchy-Gauss variation strategy was used to help the population escape from the local optimal location and find the global optimal location. To verify the performance of SI-DBO， simulation experiments were carried out on the test functions and Wilcoxon rank-sum test was performed on the experimental results， and the algorithm was used to solve robot gripper problem. Experimental results show that SI-DBO achieves higher optimization accuracy and convergence speed than Black Widow-Dung Beetle Optimization （BWDBO） algorithm and Sparrow Search Algorithm （SSA） on the test functions. Meanwhile， SI-DBO performs better than Particle Swarm Optimization （PSO） algorithm for solving robot gripper problem， indicating better optimization performance and engineering practicability of SI-DBO.

Key words: Dung Beetle Optimizer (DBO) algorithm, restricted reverse learning, Cauchy-Gauss variation, Wilcoxon rank-sum test, robot gripper problem