An Asynchronous Genetic Algorithm for Multi-agent Path Planning Inspired by Biomimicry  

作　　者：Bin Liu Shikai Jin Yuzhu Li Zhuo Wang Donglai Zhao Wenjie Ge 

机构地区：[1]School of Mechanical Engineering,Northwestern Polytechnical University,Xi’an,710072,China

出　　处：《Journal of Bionic Engineering》2025年第2期851-865,共15页仿生工程学报(英文版)

摘　　要：To address the shortcomings of traditional Genetic Algorithm (GA) in multi-agent path planning, such as prolonged planning time, slow convergence, and solution instability, this paper proposes an Asynchronous Genetic Algorithm (AGA) to solve multi-agent path planning problems effectively. To enhance the real-time performance and computational efficiency of Multi-Agent Systems (MAS) in path planning, the AGA incorporates an Equal-Size Clustering Algorithm (ESCA) based on the K-means clustering method. The ESCA divides the primary task evenly into a series of subtasks, thereby reducing the gene length in the subsequent GA process. The algorithm then employs GA to solve each subtask sequentially. To evaluate the effectiveness of the proposed method, a simulation program was designed to perform path planning for 100 trajectories, and the results were compared with those of State-Of-The-Art (SOTA) methods. The simulation results demonstrate that, although the solutions provided by AGA are suboptimal, it exhibits significant advantages in terms of execution speed and solution stability compared to other algorithms.

关 键 词：Multi-agent path planning Asynchronous genetic algorithm Equal-size clustering Genetic algorithm 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]