Abstract
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.
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Lanzarini, L., Leza, V., De Giusti, A. (2008). Particle Swarm Optimization with Variable Population Size. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_43
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DOI: https://doi.org/10.1007/978-3-540-69731-2_43
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