Multi-objective particle swarm optimization on computer grids
Proceedings of the 9th annual conference on Genetic and evolutionary computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
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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.