关键词: 蚁群算法, 聚类算法, 旅行商问题, 信息素优化, 母蚁引导

Abstract: Aiming at the problem that ant colony algorithm tends to fall into local optimal and converge slowly when solving large-scale traveling salesman problem, an ant colony algorithm combining adaptive clustering and mother ant guidance strategy was proposed.In the adaptive clustering, firstly, using the improved clustering method, using the idea of maximum and minimum distance and class density, through the adaptive clustering strategy, the best clustering result is obtained, and the optimal solution of each class is quickly obtained. Secondly, using the nearest neighbor principle, the adjacent classes are cobweb fused, thus effectively improving the accuracy of the initial solution. In addition, the mother ant guidance strategy was used to optimize the initial solution, which included path induction and pheromone optimization. Path induction set the initial solution as the first generation solution, which improved the stability of the algorithm. Pheromone optimization improves the accuracy of understanding by stimulating the initial solution path with pheromone.Experimental results show that the proposed algorithm can not only guarantee the accuracy of understanding, but also improve the stability of the algorithm when solving large-scale traveling salesman problem.

Key words: Ant colony algorithm, Clustering algorithm, Travel agent problem, Pheromone optimization, Mother ant guide