Ant Colony Optimization Using Common Social Information and Self-Memory

Ant colony optimization (ACO), which is one of the metaheuristics imitating real ant foraging behavior, is an effective method to ?nd a solution for the traveling salesman problem (TSP). The rank-based ant system (AS(rank)) has been proposed as a developed version of the fundamental model AS of ACO. In the AS(rank), since only ant agents that have found one of some excellent solutions are let to regulate the pheromone, the pheromone concentrates on a specific route. As a result, although the AS(rank) can find a relatively good solution in a short time, it has the disadvantage of being prone falling into a local solution because the pheromone concentrates on a specific route. This problem seems to come from the loss of diversity in route selection according to the rapid accumulation of pheromones to the specific routes. Some ACO models, not just the AS(rank), also suffer from this problem of loss of diversity in route selection. It can be considered that the diversity of solutions as well as the selection of solutions is an important factor in the solution system by swarm intelligence such as ACO. In this paper, to solve this problem, we introduce the ant system using individual memories (ASIM) aiming to improve the ability to solve TSP while maintaining the diversity of the behavior of each ant. We apply the existing ACO algorithms and ASIM to some TSP benchmarks and compare the ability to solve TSP