Multi-objective particle swarm optimization with dynamic population size

Abstract There are many complex multi-objective optimization problems in the real world, which difficult to solve using traditional methods. Multi-objective particle swarm is one of effective algorithms such problems. This paper proposes a with dynamic population size (D-MOPSO), helps compensate for lack convergence and diversity brought by optimization, makes full use existing resources search process. In D-MOPSO, increases or decreases depending on archive, thereby regulating size. On hand, particles added according local perturbations improve exploration. other non-dominated sorting density used control prevent excessive growth Finally, algorithm compared 13 competing four series benchmark The results show that proposed has advantages solving different