关键词: 旅行商问题, 初始种群, 邻域法, 三角概率, 自适应函数

Abstract: In order to improve the computational efficiency and solution accuracy of the TSP problem, Field Triangular Probability Choosing Adaptive Neighbor-hood Algorithm（FTPCANA） is proposed to solve the problem of initial population construction. To make the neighbor-hood radius adapt to the distribution of the city, a neighbor-hood radius adaptive function based on Sigmoid function is designed. Besides, the next city is selected by using the triangular probability selection model in the neighbor-hood, which can avoid the blind and random selection of the next station. The optimization of the storage operation sequence is selected as the research problem of TSP. Based on Matlab simulation, the initial population generated by the proposed algorithm is compared to the initial population generated by the neighbor-hood method. The two initial populations, one of which is generated by proposed algorithm, and the other is randomly generated, are respectively calculated by genetic algorithm. It is proved that the algorithm can quickly generate high quality initial population, which greatly improves the computational efficiency and accuracy of solving the TSP problem.

Key words: traveling salesman problem, initial population, neighbor-hood method, triangular probability, adaptive function