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In order to improve the optimization effect of traditional ant colony algorithm in solving traveling salesman problem, this paper developed a dual-ant colony optimization algorithm with dynamic differentiation and neighborhood induction mechanism. Firstly, the algorithm introduced chaotic random strategy, and changed the original greedy strategy in the initialization stage of the algorithm to chaotic distribute the initial pheromone, so as to maintain the diversity of the population and improve the accuracy of the solution. Secondly, the algorithm divided the isolated ants in the ant colony into isolated ant colony and normal ant colony, and the two groups of ants searched around the current optimal path and the outlier path respectively. It adopted the induction mechanism among the populations. Normal ants were responsible for searching the optimal path, and isolated ants released pheromones randomly in chaos to induce the normal ant colony to a new path, thus effectively balancing the contradiction between the convergence rate and the diversity of solutions. The simulation results of different scale TSP show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The traveling salesman problem (TSP) is a typical combinatorial optimization problem, which is often applied to sensor placement, path planning, etc. In this paper, an improved ACO algorithm based on an adaptive heuristic factor (AHACO) is proposed to deal with the TSP. In the AHACO, three main improvements are proposed to improve the performance of the algorithm. First, the k-means algorithm is introduced to classify cities. The AHACO provides different movement strategies for different city classes, which improves the diversity of the population and improves the search ability of the algorithm. A modified 2-opt local optimizer is proposed to further tune the solution. Finally, a mechanism to jump out of the local optimum is introduced to avoid the stagnation of the algorithm. The proposed algorithm is tested in numerical experiments using 39 TSP instances, and results shows that the solution quality of the AHACO is 83.33% higher than that of the comparison algorithms on average. For large-scale TSP instances, the algorithm is also far better than the comparison algorithms. [ABSTRACT FROM AUTHOR]