A Population Size Dynamic Reduction Criterion in PSO Algorithms

The size of the population is extremely important when executing a population-based algorithm. Its portioning impacts how much the algorithm will have for exploration and exploitation. An excessively large population can benefit exploration as opposed to exploitation. With a population below ideal, exploration may be impaired, and the algorithm may quickly converge to a local optimum. Unfortunately, the choice of population size is often made empirically, where the user experiences different values, several times, for different problems, without any well-defined criteria, often drawing only on his experience. This type of approach can under-use the algorithm, generating waste in both computational cost and results. In this work, we improve and study an approximation metamodel as a particle reduction criterion for particle swarm algorithms. This metamodel considers that if two particles are relatively close and with similar velocitys, they will tend to the same solution, allowing one of these to be eliminated. Five traditional benchmark problems in the literature in the field of engineering applications were performed and the results analyzed.