Application of Genetic Algorithm in TSP Problem

The large-scale operation of genetic algorithm of traveling salesman problem(traveling salesman problem,TSP) requires a lot of time,and is easy to make into local optimum.Performance of the typical genetic algorithm(GA) in solving the traveling salesman problem is not ideal.Through the improvement of the typical of crossover operator of genetic algorithm,and in order to solve TSP problem,this paper proposes an improved genetic algorithm,which dynamically adjusts the probabilities of crossover and mutation in order to reduce the possible chromosomal inbreeding coefficient,and effectively controls the evolutionary process.Compared with other algorithms,this algorithm effectively improves the speed of convergence and obtains better performance.This algorithm is verified on the TSP problem in 100 cities in China.The results show that the improved genetic algorithm has better global searching performance and uses less convergence time.