蚁群优化求解有能力车辆路径问题的遗传算法

Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence Pub Date : 2020-03-21 DOI:10.1145/3396474.3396489

Oliver Faust, Carlo Mehli, T. Hanne, Rolf Dornberger

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引用次数: 3

摘要

本文讨论了遗传算法(GA)和蚁群优化(ACO)两种元启发式算法的联合应用。遗传算法对蚁群算法参数进行优化，自动找到最优参数设置，求解给定的有能力车辆路径问题。详细阐述了本实验的研究设计和实现原型，并给出了实验结果。对不同CVRP的最优蚁群算法参数进行了计算和分析，并讨论了所提出的GA-ACO算法求解CVRP的合理性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Genetic Algorithm for Optimizing Parameters for Ant Colony Optimization Solving Capacitated Vehicle Routing Problems

This paper discusses the combined application of two metaheuristic algorithms, a Genetic Algorithm (GA) and Ant Colony Optimization (ACO). The GA optimizes ACO parameters to find the optimal parameter settings automatically to solve a given Capacitated Vehicle Routing Problem (CVRP). The research design and the implemented prototype for this experiment are explained in detail and test results are presented. Optimal ACO parameters for the different CVRP are computed and analyzed and the reasonability of the proposed GA-ACO algorithm to solve CVRP is discussed.

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Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence

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