Multi-Objective Particle Swarm Optimization Algorithm Based on Population Decomposition

In this paper, a novel multi-objective particle swarm optimization algorithm is proposed based on decomposing the objective space into a number of subregions and optimizing them simultaneously. The subregion strategy has two very desirable properties with regard to multi-objective optimization. One advantage is that the local best in the subregion can effectively guide the particles to Pareto front combining with global best. The other is that it has a better performance on the convergence and diversity of solutions. Additionally, this paper applies min-max strategy with determined weight as fitness functions to multi-objective particle swarm optimization, and there is no additional clustering or niching technique needed. In order to demonstrate the performance of the algorithm, it is compared with MOPSO and DMS-MO-PSO. The results indicate that proposed algorithm is efficient.