基于改进蝙蝠算法的AGV路径规划研究

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中图分类号：TP242.2 文献标识码：A

Abstract： The standard Bat Algorithm shows some limitations in Automated Guided Vehicle （AGV） path planning，include low optimization accuracy， slow initial convergence speed，and a tendency to fallinto local optima. An improved bat algorithm is therefore proposed. First，an adaptive dynamic inertia weight is introduced. This balances the global search and local search capabilities of the algorithm. Second，the mutation mechanism of the differential evolution algorithm is utilized. Its crossover and selection operations help preserve superior offspring. This enhances the ability to escape local optima during later iterations and improves solution accuracy. Finally，a phased search strategy is adopted. It conducts both full-dimensional and single-dimensional searches for the optimal individual，which increases the convergence eficiency of the algorithm. The improved bat algorithm is tested using standard test functions. Convergence curves and running time are analyzed. Simulations are also performed under identical conditions for comparison with the standard bat algorithm. Experimental results verify that the improved algorithm achieves approximately a 30% faster convergence speed and a 16.8% shorter path length. Furthermore，it has improved the optimization accuracy. The improved algorithm shows good practical value and application potential.

Keywords：bat algorithm；path planning；differential evolution algorithm；automated guided vehicle

在技术创新的推动下，AGV凭借高效的物料搬运能力，广泛应用于汽车制造、航空航天及港口物流等领域[1]。（剩余7365字）