Un nuevo enfoque de optimización basado en la teoría evolutiva de juegos no estructurada
DOI:
https://doi.org/10.32870/recibe.v13i1.332Palabras clave:
Optimización, Competición, Metaheurística, Teoría de Juegos, Metropolis-HastingResumen
Proponer nuevos métodos metaheurísticos es crucial para la mejora continua en el desarrollo dealgoritmos y la capacidad de abordar con eficacia problemas de optimización del mundo real cadavez mas complejos. Por otro lado, la Teoría Evolutiva de Juegos analiza cómo a través de lacompetencia es posible modificar las estrategias de los individuos dentro de una población con elfin de extender los mecanismos exitosos y reducir o eliminar las estrategias menos exitosas. Esteartículo presenta un novedoso enfoque de optimización basado en los principios de la TeoríaEvolutiva de Juegos. En el método propuesto, todos los individuos se inicializan mediante latécnica Metropolis-Hasting, que sitúa las soluciones en un punto de partida más cercano a lasregiones óptimas o casi óptimas del problema. Se asigna una estrategia original a cada individuode la población. Al tener en cuenta las interacciones y la competencia entre los distintos agentesdel problema de optimización, el enfoque modifica las estrategias para mejorar la eficiencia de labúsqueda y encontrar mejores soluciones. Para evaluar el rendimiento de la técnica propuesta, secompara con ocho algoritmos metaheurísticos bien conocidos utilizando 30 funciones de prueba.La metodología propuesta demostró superioridad en términos de calidad de la solución,dimensionalidad y convergencia en comparación con otros enfoques.Citas
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