Improved genetic algorithm for solving traveling salesman problem

Abstract

This paper proposes an improved genetic algorithm (IGA) to address the slow and unstable convergence speed of traditional genetic algorithms for solving traveling salesman problems. This algorithm optimizes the initial population through neighborhood search algorithms, designs an adaptive crossover and mutation probability, incorporates the Metropolis criterion to accept inferior solutions with a certain probability, improves the ability to jump out of local optima, and adds reversal operations to enhance local search ability and accelerate population convergence. Using MATLAB, IGA and five other algorithms were tested in the TSPLIB database. The simulation results showed that this algorithm has certain advantages in convergence speed and solution accuracy in small and medium-sized TSP problems.