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An adaptive evolutionary approach for real-time vehicle routing and dispatching

机译:实时车辆路线与调度的自适应进化方法

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摘要

The quality of the convergence process in genetic algorithms depends on the specific choice of strategies and combinations of operators. In this paper, we address this problem and introduce an adaptive evolutionary approach that uses a genetic algorithm in an adaptive process. An application of this approach to the dynamic vehicle routing problem with time windows is presented. We compare the adaptive version of a hybrid genetic algorithm with the non-adaptive one with respect to the robustness and the quality of the generated solutions. The results obtained show the ability of our operator combination adaptation approach to produce solutions that are superior to hand-tuning and other adaptive methods with respect to performance sensitivity and robustness.
机译:遗传算法中收敛过程的质量取决于策略的特定选择和运算符的组合。在本文中,我们解决了这个问题,并介绍了一种在自适应过程中使用遗传算法的自适应进化方法。提出了该方法在带时间窗的动态车辆路径问题中的应用。关于生成的解决方案的鲁棒性和质量,我们将混合遗传算法的自适应版本与非自适应遗传算法进行比较。获得的结果表明,我们的操作员组合适应方法能够产生在性能灵敏度和鲁棒性方面优于手动调整和其他自适应方法的解决方案。

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