Improved Ant Colony Algorithm with Emphasis on Data Processing and Dynamic City Choice

To resolve the contradictory among accelerating convergence, premature and stagnation in conventional ant colony algorithm, this paper proposes a novel ant colony algorithm with emphasis on data processing and dynamic city choice. Considering the importance of distance data, the proposed algorithm processes the data effectively. And it introduces symmetry and the number of allowed paths to adaptively adjust the strategy of city choice and the strategy of pheromone update, in accordance with the distribution of solutions during the optimizing process. Experimental results of the traveling salesman problem with large-scale data show that the improved algorithm has better ability of global searching, lager convergence speed and better solution diversity than that of conventional ant colony algorithm.