Keywords
unconstrained optimization
constrained optimization
multiobjective optimization
fuzzy numbers
optimización por enjambres de partículas
optimización sin res- tricciones
optimización con restricciones
optimización multiobjetivo
How to Cite
Abstract
In this paper a novel system of convergence (SC) is presented as well as its fundamentals and computing experience. An implementation using a novel mono-objetive particle swarm optimization (PSO) algorithm with three phases (PSO-3P): stabilization, generation with broad-ranging exploration and generation with in-depth exploration, is presented and tested in a diverse benchmark problems. Evidence shows that the three-phase PSO algoritm along with the SC criterion (SC-PSO-3P)can converge to the global optimum in several difficult test functions for multiobjective optimization problems, constrained optimization problems and unconstrained optimization problems with 2 until 120,000 variables.
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