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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Multi-objective particle swarm optimization (MOPSO) is an optimization technique inspired by bird flocking, which has been steadily gaining attention from the research community because of 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 an modified multi-objective particle swarm optimizer named MMOPSO, for dealing with multi-objective problems. we introduce some ideas concerning the guide selection for each particle. The proposed algorithm is compared against four multi-objective evolutionary approaches based on particle swarm optimization on four benchmark problems. The numerical results show the effectiveness of the proposed MMOPSO algorithm.