Improving the performance of ACO algorithms by adaptive control of candidate set

The performance of ant colony optimization (ACO) algorithms with candidate sets is high for large optimization problems, but it is difficult to set the size of candidate sets to optimal in advance. We propose an adaptive control mechanism of candidate sets based on pheromone concentrations for improving the performance of ACO algorithms and report the results of computational experiments using the graph coloring problems.