Abstract
Particle swarm optimization(PSO)algorithm is a based on the population evolutionary algorithm, which has gained wide attentions in a variety of fields for solving multi-objective optimization problem because of its simplicity to implement and its high convergence speed. However, faced with multi-objective problems, adaptations are needed. Deeper researches must be conducted on its key steps, such as guide selection, in order to improve its efficiency in this context. This paper proposes a multi-objective particle swarm optimizer based on differential populations named MOPSODP, for dealing with multi-objective problems. we introduce some ideas concerning the guide selection for each particle. The proposed algorithm is compared against three multi-objective evolutionary approaches based on particle swarm optimization. The numerical results show the effectiveness of the proposed algorithm.
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