关键词: 遗传算法, 模拟退火算法, 旅行商问题（TSP）, 优化算法, 最优解

Abstract: The characteristics of genetic algorithm and simulated annealing algorithm are compared, it elaborates the necessity of the combination of genetic algorithm and simulated annealing algorithm. An improved simulated annealing and genetic algorithm for solving TSP is proposed. The global random searching ability of genetic algorithm makes up the question of the simulated annealing algorithm that easy to fall into the local optimal solution. The crossover method of genetic algorithm is changed, the parent chromosomes and offspring chromosomes are crossed, it solves the problems of traditional genetic algorithm “premature”. It proposes new solution generation mechanisms and improved algorithm for that the simulated annealing algorithm converges slowly, the method improves the speed of convergence of the algorithm. Experimental test results show that the new algorithm has faster convergence and better stability.

Key words: genetic algorithm, simulated annealing algorithm, Traveling Salesman Problem（TSP）, optimization algorithm, optimal solution