An ant colony optimization algorithm based on mutation and dynamic pheromone updating

Despite the numerous applications of ACO (ant colony optimization) algorithm in optimization computation, it remains a computational bottleneck that the ACO algorithm costs too much time in order to find an optimal solution for large-scaled optimization problems. Therefore, a quickly convergent version of the ACO algorithm is presented. A novel strategy based on the dynamic pheromone updating is adopted to ensure that every ant contributes to the search during each search step. Meanwhile, a unique mutation scheme is employed to optimize the search results of each step. The computer experiments demonstrate that the proposed algorithm makes the speed of convergence hundreds of times faster than the latest improved ACO algorithm.