SC-System of convergence theory and foundations

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 gener...

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Main Author: Sergio G. De los Cobos Silva
Format: Article
Language:English
Published: Universidad de Costa Rica 2015-08-01
Series:Revista de Matemática: Teoría y Aplicaciones
Subjects:
Online Access:https://revistas.ucr.ac.cr/index.php/matematica/article/view/20845
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author Sergio G. De los Cobos Silva
author_facet Sergio G. De los Cobos Silva
author_sort Sergio G. De los Cobos Silva
collection DOAJ
description 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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spelling doaj.art-94fd8e2440c54c0fbdaecc4b0f7413032023-09-02T05:45:24ZengUniversidad de Costa RicaRevista de Matemática: Teoría y Aplicaciones2215-33732015-08-0122234136710.15517/rmta.v22i2.2084518908SC-System of convergence theory and foundationsSergio G. De los Cobos Silva0Universidad Autónoma Metropolitana-Iztapalapa, Departamento de Ingeniería Eléctrica, Av. San Rafael Atlixco 186, Col. Vicentina, Del. Iztapalapa, México D.F.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.https://revistas.ucr.ac.cr/index.php/matematica/article/view/20845particle swarm optimizationunconstrained optimizationconstrained optimizationmultiobjective optimizationfuzzy numbers
spellingShingle Sergio G. De los Cobos Silva
SC-System of convergence theory and foundations
Revista de Matemática: Teoría y Aplicaciones
particle swarm optimization
unconstrained optimization
constrained optimization
multiobjective optimization
fuzzy numbers
title SC-System of convergence theory and foundations
title_full SC-System of convergence theory and foundations
title_fullStr SC-System of convergence theory and foundations
title_full_unstemmed SC-System of convergence theory and foundations
title_short SC-System of convergence theory and foundations
title_sort sc system of convergence theory and foundations
topic particle swarm optimization
unconstrained optimization
constrained optimization
multiobjective optimization
fuzzy numbers
url https://revistas.ucr.ac.cr/index.php/matematica/article/view/20845
work_keys_str_mv AT sergiogdeloscobossilva scsystemofconvergencetheoryandfoundations