Multi-AGV path planning with double-path constraints by using an improved genetic algorithm

Zengliang Han, Dongqing Wang, Feng Liu, Zhiyong Zhao

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    82 Scopus citations

    Abstract

    This paper investigates an improved genetic algorithm on multiple automated guided vehicle (multi-AGV) path planning. The innovations embody in two aspects. First, three-exchange crossover heuristic operators are used to produce more optimal offsprings for getting more information than with the traditional two-exchange crossover heuristic operators in the improved genetic algorithm. Second, double-path constraints of both minimizing the total path distance of all AGVs and minimizing single path distances of each AGV are exerted, gaining the optimal shortest total path distance. The simulation results show that the total path distance of all AGVs and the longest single AGV path distance are shortened by using the improved genetic algorithm.

    Original languageEnglish
    Article numbere0181747
    JournalPLoS ONE
    Volume12
    Issue number7
    DOIs
    StatePublished - Jul 2017

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