关键词: 蚁群算法, 双种群, 商旅问题（TSP）, 协同过滤, 城市失活

Abstract: Aiming at the problems that the ant colony algorithm is easy to fall into local optimum and the convergence speed is slow, a heterogeneous dual population ant colony algorithm based on cooperative filtering strategy is proposed. For the two heterogeneous populations, a collaborative filtering strategy is introduced to reward the more preferred paths of ants in the two populations, which is used for making the algorithm more oriented and speeding up the convergence of the algorithm; the frequency of dual population information exchange is adaptively adjusted based on the dynamic feedback of information between populations and the diversity of algorithm is increased. When the algorithm is stagnant, each population pheromone is averaged according to the dynamic interaction feedback between the populations, and the local optimum is jumped out. Finally, according to the idea of neural network inactivation, the method of city-wide inactivation is used to make the program run time shorter. In the medium and large scale traveling salesman problem (TSP) experiment, the algorithm improves the quality of the understanding, ensures the diversity of the algorithm, and accelerates the convergence speed of the algorithm.

Key words: ant colony algorithm, dual population, traveling salesman problem (TSP), collaborative filtering, city inactivation