Swarm intelligence
Particle swarm optimization method in multiobjective problems
Proceedings of the 2002 ACM symposium on Applied computing
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
Multi-objective evolutionary guidance for swarms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A non-dominated sorting particle swarm optimizer for multiobjective optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Fuzzy-Pareto-Dominance and its application in evolutionary multi-objective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A MOPSO algorithm based exclusively on pareto dominance concepts
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
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This paper introduces a new approach to multi-objective ParticleSwarm Optimization (PSO). The approach is based on the recentlyproposed Fuzzy-Pareto-Dominance (FPD) relation. FPD is a genericranking scheme, where ranking values are mapped to element vectorsof a set. These ranking values are directly computed from theelement vectors of the set and can be used to perform rankoperations (e.g. selecting the "largest") with the vectors withinthe given set. FPD can be seen as a paradigm or meta-heuristic toformally expand single-objective optimization algorithms tomulti-objective optimization algorithms, as long as suchvector-sets can be defined. This was already shown for the StandardGenetic Algorithm. Here, we explore the application of this conceptto PSO, where a swarm of particles is maintained. The resultingPSO_{f2r} algorithm is studied on a fundamental optimizationproblem (so-called Pareto-Box-Problem) where a complete analysis ispossible. The PSO_{f2r} algorithm is shown to handle the case of alarger number of objectives, and shows similar properties like the(single-objective) PSO.