Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Adaptively choosing niching parameters in a PSO
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Varying the population size of artificial foraging swarms on time varying landscapes
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
VarMOPSO: multi-objective particle swarm optimization with variable population size
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
A new binary PSO with velocity control
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
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At present, the optimization problem resolution is a topic of great interest, which has fostered the development of several computer methods forsolving them.Particle Swarm Optimization (PSO) is a metaheuristics which has successfully been used in the resolution of a wider range of optimization problems, including neural network training and function minimization. In its original definition, PSO makes use, during the overall adaptive process, of a population made up by a fixed number of solutions.This paper presents a new extension of PSO, called VarPSO, incorporating the concepts of age and neighborhood to allow varying the size of the population. In this way, the quality of the solution to be obtained will not be affected by the used swarm's size.The method here proposed is applied to the resolution of some complex functions, finding better results than those typically achieved using a fixed size population.