Traditional genetic algorithm has become one of the preferred choices to solve the travelling salesman problem because of its powerful global search ability,but its poor local search ability limits the effectiveness of the algorithm in seeking the optimal solution.To solve this problem,this paper optimizes the initial solution through a modified circle algorithm and the probabil-ity of an adaptive adjustment to perform each genetic operation during evolution;by combining the key step of the simulated annea-ling algorithm with the metropolis criterion and incorporating the reversal operation,the genetic algorithm based on the random simu-lation strategy is improved and applied to solve the traveling traveler problem.The simulation results show that the improved genetic algorithm is superior to the traditional genetic algorithm in convergence speed,convergence effect and quality.
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