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arXiv · 2107.06870

Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem

Jiongzhi Zheng·Jialun Zhong·Menglei Chen·Kun He
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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.

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Jiongzhi Zheng, Jialun Zhong, Menglei Chen, Kun He. 2021-07-09. Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem. https://arxiv.org/abs/2107.06870

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