Metropolis-Hastings (MH): Una Perspectiva Innovadora en la Inicialización de Poblaciones
DOI:
https://doi.org/10.32870/recibe.v13i1.335Palabras clave:
Métodos de inicialización, Algoritmos metaheurísticos, OptimizaciónResumen
En este artículo, se propone un nuevo método de inicialización de poblaciones para algoritmos metaheurísticos. En este enfoque, el conjunto inicial de soluciones iniciales se obtiene a través del muestreo de la función objetivo aplicando la técnica de Metropolis-Hastings (MH). Bajo este método, el conjunto inicial de soluciones adopta un valor cercano a los valores prominentes de la función objetivo a optimizar. A diferencia de la mayoría de los métodos de inicialización que únicamente consideran una distribución espacial, en el método, los puntos iniciales representan regiones promisorias del espacio de búsqueda, las cuales merecen ser explotadas para identificar la solución óptima global de una manera más rápida. brindando al algoritmo una convergencia más rápida y mejorando la calidad de las soluciones obtenidas. Con el objetivo de demostrar el rendimiento del método de inicialización a algoritmos metaheurísticos, éste ha sido embebido en el algoritmo de Differential Evolution (DE) clásico, y el sistema completo ha sido puesto a prueba en un conjunto representativo de funciones de benchmark extraído de diferentes conjuntos de datos. Los resultados experimentales demuestran una mejora en la rapidez de convergencia y un incremento en la calidad de las soluciones por parte del enfoque propuesto, a comparación de otros métodos similares.Citas
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