Ant supervised by PSO

Swarm-inspired optimization has become an attractive research field. Since most real world problems are multi criteria ones´, multi-objective algorithms seem to be the most fitted to solve them. Particle swarm optimization (PSO) and ant colony optimization (ACO) have attracted the interest of researchers. Our proposal is to make PSO supervising an ant optimizer. In this paper we propose an Ant colony algorithms supervised by particle swarm optimization to solve continuous optimization problems. Traditional ACO are used for discrete optimization while PSO is for continuous optimization problems. Separately, PSO and ACO shown great potential in solving a wide range of optimization problems. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm rdquoant supervised by PSOrdquo (A.S.PSO) the proposed algorithm can reduce the probability of being trapped in local optima and enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. Pheromone deposit by the ants´ mechanisms would be used by the PSO as a weight of its particles ensuring a better global search strategy. By using the A.S.PSO design method, ants supervised by PSO in the feasible domain can explore their chosen regions rapidly and efficiently.