Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Multiobjective optimization using a Pareto differential evolution approach
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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In the past few years, a number of researchers have successfully extended particle swarm optimization to multiple objectives. However, it still is an important issue to obtain a well-converged and well-distributed set of Pareto-optimal solutions. In this paper, we propose a fuzzy particle swarm optimization algorithm based on fuzzy clustering method and fuzzy strategy and archive update. The particles are evaluated and the dominated solutions are stored into different cluster in the generation, while dominated solutions are pruned. The non-dominated solutions are selected by fuzzy strategy, and the nondominated solutions are added to the archive. It is observed that the proposed fuzzy particle swarm optimization algorithm is a competitive method in the terms of convergence near to the Pareto-optimal front, diversity of solutions.