Research on Path Planning Based on the FEGDBO Algorithm

To enhance the performance of path planning problems, this paper proposes a Multi-strategy Enhanced Dung Beetle Optimization Algorithm (FEGDBO). This algorithm integrates Tent chaotic mapping for initialization, adaptive candidate search for exploration–exploitation balance, Differential Evolution perturbation, and a Golden Sine position update strategy to enhance global optimization capability and convergence efficiency. Experimental validation shows that FEGDBO outperforms traditional optimization algorithms and various advanced improved algorithms in terms of convergence speed, solution accuracy, and overall optimization performance. As a representative application case, this paper applies FEGDBO to the global path planning problem of warehouse AGVs, and the results confirm the effectiveness and applicability of the algorithm in practical AGV path planning tasks.