Path Optimization for Hole Group Machining Based on Improved Dung Beetle Optimization Algorithm

The dung beetle optimization technique boasts simplicity in its principles, robust search capabilities, and swift optimization. However, it suffers from drawbacks such as limited global search and susceptibility to local optimization traps. The paper introduces an enhanced dung beetle optimization technique to address its limitations. Initially, it incorporates the global exploration strategy from the Fish and Eagle optimization algorithm to enhance the dung beetles' global search capability and expand their search range. Subsequently, it integrates the local exploration strategy of the Fish and Eagle optimization algorithm to bolster the algorithm's local search performance and its capacity to escape local optima. Moreover, the algorithm enhances its search capability by refining the dung beetles' position update mechanism through the incorporation of Levy flights and a t-distribution perturbation strategy. Mechanism enhances its search capability and scope, making the algorithm more comprehensive and effective in coping with the problems of global search and falling into local optimal solutions. The enhanced dung beetle optimization algorithm undergoes compared with 15 test functions in CEC2005 with 7 unimodal, 5 multimodal, and 3 composite functions, and finally the algorithm is applied to hole swarm machining, and the improved algorithm has a better optimization performance and convergence speed.