An Improved genetic algorithm for solving TSP

Abstract

An improved genetic algorithm is proposed to solve the traveling salesman problem. On the basis of traditional genetic algorithm, greedy algorithm is introduced to initialize the population. The genetic parameters are adjusted adaptively from two aspects of genetic evolution algebra and individual fitness function value, which can speed up the optimization speed and prevent the optimization from falling into local optimum. In mutation operation, adaptive algorithm is used to select mutation operator to improve the quality of variation and search effect of the algorithm; after individual evolution, one-way evolutionary reversal operation is introduced to improve the chance of inheriting parent and parent genes, and improve the ability of algorithm to search for optimal solution.