Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem

TL;DR

This paper introduces a novel Reinforced Hybrid Genetic Algorithm (RHGA) that combines reinforcement learning, genetic algorithms, and local search heuristics to efficiently solve large-scale Traveling Salesman Problems, outperforming existing methods.

Contribution

The paper presents a new hybrid algorithm integrating reinforcement learning with genetic algorithms and local search, enhancing solution quality and efficiency for large TSP instances.

Findings

RHGA achieves superior solutions on 138 benchmark TSP instances.

The hybrid mechanism improves convergence speed and solution diversity.

Experimental results demonstrate RHGA's effectiveness on large-scale problems.

Abstract

In this paper, we propose a new method called the Reinforced Hybrid Genetic Algorithm (RHGA) for solving the famous NP-hard Traveling Salesman Problem (TSP). Specifically, we combine reinforcement learning with the well-known Edge Assembly Crossover genetic algorithm (EAX-GA) and the Lin-Kernighan-Helsgaun (LKH) local search heuristic. In the hybrid algorithm, LKH can help EAX-GA improve the population by its effective local search, and EAX-GA can help LKH escape from local optima by providing high-quality and diverse initial solutions. We restrict that there is only one special individual among the population in EAX-GA that can be improved by LKH. Such a mechanism can prevent the population diversity, efficiency, and algorithm performance from declining due to the redundant calling of LKH upon the population. As a result, our proposed hybrid mechanism can help EAX-GA and LKH boost each other's performance without reducing the convergence rate of the population. The reinforcement learning technique based on Q-learning further promotes the hybrid genetic algorithm. Experimental results on 138 well-known and widely used TSP benchmarks with the number of cities ranging from 1,000 to 85,900 demonstrate the excellent performance of RHGA.