Enhancement of the Multi-Particle Collision Algorithm by mechanisms derived from the Opposition-Based Optimization

Reynier Hernández Torres, Haroldo F. Campos Velho, Eduardo F. P. da Luz

Resumen


En este trabajo se presentan nuevas versiones de la metaheurística Algoritmo de Colisión de Múltiples Partículas (MPCA). Para proporcionar soluciones candidatas más efectivas para un problema de optimización, se introduce el concepto de oposición y reflexión, con el objetivo de mejorar la capacidad de encontrar una solución en el espacio de búsqueda. Se implementan cuatro estrategias diferentes para calcular los puntos reflejados y opuestos. El rendimiento de todas las implementaciones se evalúa en más de treinta funciones objetivo con diferentes complejidades, utilizando versiones en serie y paralelas de los algoritmos.

Palabras clave


Algoritmo estocástico; Metaheurística; Aprendizaje basado en oposición; Aprendizaje basado en reflexión; Algoritmo de Colisión de Múltiples partículas

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Referencias


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DOI: http://dx.doi.org/10.17268/sel.mat.2019.02.03

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