MAX-MIN Ant System with Two Memories Considering Ant Decision-Making by Social and Individual Information

One method for solving combinatorial optimization problems is Ant Colony Optimization (ACO), which models the ants' habit of efficient foraging behavior through global communication via pheromones. However, conventional ACO does not take into account important ant decision-making processes other than global communication via pheromones. Therefore, we propose a new ACO that introduces into the model decision-making processes based on both social information (information obtained through global and local communication) and individual information (ants' own past experience), which are considered important for ants in the real world. In evaluation experiments, we applied the proposed ACO to the traveling salesman problem, a typical combinatorial optimization problem, and confirmed that the solution search performance is significantly improved compared to conventional methods. This indicates that the approach of modeling ants' decision-making based on social and individual information is effective in ACO. In addition, we believe that our approach to algorithm construction, which incorporates interactions between individuals into the model, has shown the potential to be effective in ACOs.