A multi-objective particle swarm optimizer based on decomposition

The simplicity and success of particle swarm optimization (PSO) algorithms, has motivated researchers to extend the use of these techniques to the multi-objective optimization field. This paper presents a multi-objective particle swarm optimization (MOPSO) algorithm based on a decomposition approach, which is intended for solving continuous and unconstrained multi-objective optimization problems (MOPs). The proposed decomposition-based multi-objective particle swarm optimizer (dMOPSO), updates the position of each particle using a set of solutions considered as the global best according to the decomposition approach. dMOPSO is mainly characterized by the use of a memory reinitialization process which aims to provide diversity to the swarm. Our proposed approach is compared with respect to two decomposition-based multi-objective evolutionary algorithms (MOEAs) which are representative of the state-of-the-art in the area. Our results indicate that our proposed approach is competitive and it outperforms the two MOEAs with respect to which it was compared in most of the test problems adopted.