A Proposal of Max-Min Ant System for Keeping Diversity of Search Performance Using Search Histories

Abstract

Meta-heuristics is powerful technique for acquiring a semi-optimal solution in a usable time for a large and complex system, and has many strategies such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO) and so on. Max-Min Ant System (MMAS) is one of improved ACO algorithms, which this algorithm sets up the range of the pheromone information for preventing overconcentration of the search area. However, MMAS has the limitation of search performance after convergence of pheromone information. In this paper, an improved MMAS using the search histories is proposed for keeping the diversity of search performance. The proposed method divides into two search groups after convergence of pheromone information, and one is the diversity group which changes new rule of pheromone update considering the search histories and the other is concentration group which uses the conventional rule of pheromone update. In the computational experiments, the proposed method has the potential to keep the search performance and diversity, and to provide the better solution.