Dataset Information

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

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.

SUBMITTER: Han Z

PROVIDER: S-EPMC5528885 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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

Han Zengliang Z Wang Dongqing D Liu Feng F Zhao Zhiyong Z

PloS one 20170726 7

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 ...[more]

PMID: 28746355

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Dataset Information

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

Publications

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

Similar Datasets

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