Work

A Q-Learning-Assisted Evolutionary Optimization Method for Solving the Capacitated Vehicle Routing Problem

Year: 2025

Type: article

Abstract: The Capacitated Vehicle Routing Problem (CVRP) is a classic combinatorial optimization problem in logistics and distribution, with significant theoretical and practical importance. To address the limitations of traditional evolutionary algorithms—particularly their use of fixed operator selection and simplistic search strategies—this paper proposes a Q-learning-based evolutionary algorithm (QEA). By incorporating a reinforcement learning mechanism, the QEA adaptively selects among multiple neighborhood search operators, effectively balancing global exploration and local exploitation. In addition, a novel insertion-based crossover operator and a set of diverse neighborhood search strategies are designed to further enhance solution quality and search efficiency. Experimental results on a variety of standard CVRP benchmark instances show that the QEA demonstrates a superior performance and strong robustness, significantly outperforming several representative state-of-the-art algorithms for solving the CVRP. These results confirm the effectiveness and practical value of the proposed method.

Source: Applied Sciences

DOI: 10.3390/app15179332

Authors: Wanqiu Zhao, Zhaohui Zhang, Hong Zhao, Xu Bian

Institutions: Xi'an Jiaotong University, Xidian University, Guangzhou Institute of Technology

Language: English

Topic: Vehicle Routing Optimization Methods

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896