Reinforced Hybrid Genetic Algorithm for the Traveling Salesman Problem
This work addresses a classic NP-hard optimization problem for operations research and logistics, but it is incremental as it builds on existing methods like EAX-GA and LKH.
The paper tackled the Traveling Salesman Problem by proposing a Reinforced Hybrid Genetic Algorithm that combines reinforcement learning with genetic algorithms and local search, achieving excellent performance on benchmarks with up to 85,900 cities.
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.