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

TL;DR

This paper introduces a feature-guided metaheuristic for the Capacitated Vehicle Routing Problem that uses Explainable AI to improve solution diversity and performance across different algorithms, especially on large instances.

Contribution

It presents a novel feature-based guidance mechanism leveraging XAI to enhance metaheuristics for CVRP, demonstrating scalability and transferability across algorithms.

Findings

Significant performance improvements on benchmark instances.

Effective guidance enhances solution diversity and quality.

Mechanism is scalable to large problem sizes.

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.