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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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| País: | Costa Rica |
| Institución: | Universidad de Costa Rica |
| Repositorio: | Portal de Revistas UCR |
| Idioma: | inglés |
| OAI Identifier: | oai:portal.ucr.ac.cr:article/20845 |
| Acceso en línea: | https://revistas.ucr.ac.cr/index.php/matematica/article/view/20845 |
| Access Level: | acceso abierto |
| Palabra clave: | particle swarm optimization unconstrained optimization constrained optimization multiobjective optimization fuzzy numbers optimización por enjambres de partículas optimización sin res- tricciones optimización con restricciones optimización multiobjetivo |
| Sumario: | 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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