Pheromone-distribution-based adaptive ant colony system
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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An improved ant colony optimization algorithm is proposed in this paper. Comparing with the conventional ant colony optimization algorithm, the proposed method has two highlights. First, a newly strategy based on the dynamic control of solution construction is adopted. The purpose of this strategy is to ensure ants to exploit the solutions at the beginning of searching procedure with large probability while at the end of the searching procedure the solutions provided by each ant are obtained by searching around the best-so-far solution. Second, to obtain a more reasonable solution, a mergence mechanism, based on the local search result of each ant, is employed. The experiments demonstrate that the proposed method has better performance than the conventional ACO algorithm.