The use of metaheuristics in Multi-objective Combinatorial Optimization, particularly Ant Colony Optimization (ACO), has grown recently. This paper proposes an approach where multi-species ants compete for food resources. Each species has its own search strategy and do not access pheromone information of other species. As in nature, successful ant populations are allowed to grow, whereas the others shrink. This approach is applied to the Multi-objective Shortest Path Problem and shows to inherit the behavior of succesful strategies from different types of problems. It is also compared to an existing ACO and to NSGA-II. Results show that the proposed ap- proach is able to produce significantly better approximation sets than other methods.
Citation
Technical Report No UFRN-DIMAp-2011-104-RT. Departamento de Informática e Matemática Aplicada, Universidade Federal do Rio Grande do Norte, Natal, RN, Brazil. April, 2011.