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 speed, low precision and local optimum for the Dung Beetle Optimizer (DBO) algorithm, dung beetle optimizer algorithm with restricted reverse learning and Cauchy-Gauss variation (SI-DBO) was proposed in this paper. Firstly, Circle mapping was used to initialize the population to make the 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 location of dung beetles to improve their search ability. Finally, the 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. The algorithm was also used to solve the robot gripper problem. Experimental results show that the SI-DBO achieves higher optimization accuracy and convergence speed than the improved dung beetle optimizer (BWDBO) and Sparrow Search Algorithm (SSA) for the test functions. Meanwhile, the SI-DBO performs better than the Particle Swarm Optimization (PSO) for the robot gripper problem, indicating desirable optimization performance and engineering practicability of the SI-DBO.

Key words: dung beetle optimizer algorithm, restricted reverse learning, Cauchy-Gauss variation, Wilcoxon rank sum test, robot gripper problem