We propose a metaheuristic algorithm enhanced with feature-based guidance that is designed to solve the Capacitated Vehicle Routing Problem (CVRP). To formulate the proposed guidance, we developed and explained a supervised Machine Learning (ML) model, that is used to formulate the guidance and control the diversity of the solution during the optimization process. We propose a metaheuristic algorithm combining neighborhood search and a novel mechanism of hybrid split and path relinking to implement the proposed guidance. The proposed guidance has proven to give a statistically significant improvement to the proposed metaheuristic algorithm when solving CVRP. Moreover, the proposed guided metaheuristic is also capable of producing competitive solutions among state-of-the-art metaheuristic algorithms.

翻译：本文提出了一种基于特征引导增强的元启发式算法，用于求解容量约束车辆路径问题。为构建该引导机制，我们开发并阐释了一种监督机器学习模型，该模型用于在优化过程中构建引导策略并控制解的多样性。我们提出了一种结合邻域搜索与新型混合分割-路径重连机制的元启发式算法，以实现所提出的引导策略。实验证明，在求解容量约束车辆路径问题时，所提出的引导机制能为元启发式算法带来统计意义上显著的性能提升。此外，该引导式元启发式算法在当前最先进的元启发式算法中亦能产生具有竞争力的解。