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| Improving Genetic Algorithm for Solving Travel Salesman Problem |
| ZHU Jianjun, WANG Zhibin
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| (School of Information and Control Engineering, Jilin Institute of Chemical Technology , Jilin City132022, China) |
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Abstract An improved Genetic Algorithm (GA) is proposed to solve the problems of poor initial solution,local optima and low aceuracy of the traditional GA for solving the Travelling Salesman Problem(TSP).Firstly,the greedy algorithm is combined with elite selection to improve the quality of the initial solutions in the population. Secondly , the steps of adaptive crossover and mutation probability are designed, and the Metropolis criterion is combined to prevent the genetic algorithm from falling into local optima.Finally, Bidirectional Three- Cross of Greedy Algorithm and 2-opt search algorithm are added to improve the accuracy The results show that the deviation rate of the optimal solution of the improved genetic algorithm is less than 1.88%, and the deviation rate of the mean is less than 2.27%.
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Published: 05 July 2025
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