Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
An effective use of crowding distance in multiobjective particle swarm optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Two-level of nondominated solutions approach to multiobjective particle swarm optimization
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Particle Swarm Optimization with Variable Population Size
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Multi-Objective Particle Swarm Optimizers: An Experimental Comparison
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Evolutionary multiobjective optimization for emergency medical services
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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The PSO (Particle Swarm Optimization) metaheuristics, originally defined for solving single-objective problems, has been applied to multi-objective problems with very good results. In its initial conception, the algorithm has a fixed-size population. In this paper, a new variation of this metaheuristics, called VarMOPSO (Variable Multi-Objective Particle Swarm Optimization), characterized by a variable-sized population, is proposed. To this end, the concepts of age and neighborhood are incorporated to be able to modify the size of the population for the different generations. This algorithm was compared with the version that uses fixed-size populations, as well as with other metaheuristics, all of them representative of the state of the art in multi-objective optimization. In all cases, three widely used metrics were considered as quality indicators for Pareto front. The results obtained were satisfactory.