Abstract
The traveling salesman problem is one of the classic problems of graph theory in the field of operations research. In real life, many practical application problems, such as delivery routes of express companies, can be modelled as traveling salesman problems through simplified processing. The differential evolution algorithm is a kind of optimization algorithm that has emerged not long ago. Based on the idea of standard differential evolution algorithm, we design an evolutionary algorithm for solving the traveling salesman problem. According to different mutation strategies, new mutation operators are mixed, and adaptive strategies are added to make the parameters dynamic, and the algorithm is applied to the two cases of TSPLIB. Finally, the results are compared with other mainstream basic algorithms. The algorithm results show that the algorithm we designed has better performance for solving small-scale problems, can solve large-scale traveling salesman problems, and has stronger optimization ability than other mainstream algorithms. However, it will fall into a local optimum, and the stability of the algorithm needs to be improved.