Abstract: Traveling salesman problem(TSP) is a classic combinatorial optimization problem known for its significant computational complexity.Traditional genetic algorithms heavily rely on empirical parameter adjustments when solving the TSP,and premature reduction of population diversity will lead to local convergence,severely impacting algorithm performance.Therefore,this paper proposes an adaptive hybrid genetic algorithm(AHGA) that adjusts the key parameters of genetic algorithms adaptively with reinforcement learning.Firstly,an adaptive parameter adjustment model based on genetic algorithms is constructed.Proximal policy optimization(PPO) algorithm is employed to generate action policies controlling population evolution.Secondly,a hybrid operator is introduced to traditional genetic algorithm crossover and mutation to enhance population diversity in later iterations.Finally,the effectiveness and performance of the algorithm are validated on various TSPLIB public instances.The results demonstrate that the proposed algorithm significantly improves the solution quality and convergence speed of genetic algorithms,effectively avoiding local convergence issues,and outperforms similar algorithms in solving TSP.

Key words: Traveling salesman problem, Genetic algorithm, Proximal policy optimization, Hybrid operator, Parameter adaptation