Search

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]