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arXiv · 2407.20777

Feature-Guided Metaheuristic with Diversity Management for Solving the Capacitated Vehicle Routing Problem

Bachtiar Herdianto·Romain Billot·Flavien Lucas·Marc Sevaux
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Abstract

We propose a feature-based guidance mechanism to enhance metaheuristic algorithms for solving the Capacitated Vehicle Routing Problem (CVRP). This mechanism leverages an Explainable AI (XAI) model to identify features that correlate with high-quality solutions. These insights are used to guide the search process by promoting solution diversity and avoiding premature convergence. The guidance mechanism is first integrated into a custom metaheuristic algorithm, which combines neighborhood search with a novel hybrid of the split algorithm and path relinking. Experiments on benchmark instances with up to $30,000$ customer nodes demonstrate that the guidance significantly improves the performance of this baseline algorithm. Furthermore, we validate the generalizability of the guidance approach by integrating it into a state-of-the-art metaheuristic, where it again yields statistically significant performance gains. These results confirm that the proposed mechanism is both scalable and transferable across algorithmic frameworks.

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Bachtiar Herdianto, Romain Billot, Flavien Lucas, Marc Sevaux. 2024-07-30. Feature-Guided Metaheuristic with Diversity Management for Solving the Capacitated Vehicle Routing Problem. https://doi.org/10.1016/j.ejor.2025.12.029

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