Solution of TSP problem based on hybrid genetic simulated annealing algorithm

TSP is a classical NP-hard combinatorial optimization problem.Genetic algorithm is a method for solving this problem.But it is hard for genetic algorithm to find global optimization quickly and prevent premature convergence.This paper,in order to solve the problem,considers the characteristic of TSP,and puts forward a genetic simulated annealing algorithm,a hybrid of genetic and simulated annealing algorithm.In order to solve the inconsistency between diversity and convergent speed,this paper also adopts part greedy method to produce original population.The original population produced by this method is superior to the randomly produced original population.The simulation results demonstrate that,the proposed algorithm achieves considerable improvements,with respect to the basic genetic algorithm,in convergence speed,search quality and optimal solution output rate.