Un estudio comparativo sobre evolución diferencial auto-adaptativa en ambientes dinámicos

• Dr. C Pavel Novoa-Hernández ^[1] ; Dr. C Carlos Cruz Corona ^[2] ; Dr. C David A. Pelta ^[2]
  1. [1] Universidad de Holguín

     Universidad de Holguín

     Cuba

     
  2. [2] Universidad de Granada

     Universidad de Granada

     Granada, España

     
• Localización: Revista Cubana de Ciencias Informáticas, ISSN-e 2227-1899, Vol. 8, Nº. 4, 2014
• Idioma: español
• Títulos paralelos:
  • A comparative study on self-adaptive differential evolution in dynamic environments
• Enlaces
  • Texto completo
• Resumen
  • español

    Un gran número de problemas de optimización reales son dinámicos, lo que significa que algunos de los elementos del modelo varían con el tiempo. Estos problemas han recibido un especial interés en los últimos años desde el punto de vista de la optimización mediante metaheurísticas. La Evolución Diferencial (DE) es una metaheurística poblacional que sobresale por su efectividad y fácil implementación. Sin embargo, como la mayoría de los paradigmas aplicados a este contexto, DE ha tenido que ser adaptada con el objetivo resolver principalmente la pérdida de diversidad en la población de soluciones. Por otro lado, la auto-adaptación es uno los enfoques menos tratados en la actualidad, y que ha mostrado ser en extremo eficiente en determinados contextos. La auto-adaptación es una técnica de control de parámetros que dota de cierta inteligencia al algoritmo, durante el proceso de búsqueda. En ese sentido, el presente trabajo investiga dos extensiones auto-adaptativas de DE en combinación con otros enfoques de diversidad existentes. Los resultados, obtenidos a partir de varios experimentos computacionales, confirman que la auto-adaptación es una técnica prometedora en este contexto.

  • English

    Several real optimization problems are dynamic, meaning that some elements of their mathematical model are time varying. These problems have received a special interest in the last years from the viewpoint of metaheuristics. Differential Evolution (DE) is one of the current population-based metaheuristics with an excellent effectiveness and easy implementation. However, as similar paradigms in dynamic environments, DE has been adapted with aims of solving the diversity loss in the solution population. On the other hand, self-adaptation is one of the less used approaches in dynamic environments, despite its success in complex scenarios. Self-adaptation is a parameter control technique that gives certain intelligent behavior to the algorithm, during the search process. In that sense, the present work investigates the performance of two self-adaptive extensions of DE, which in combination with other existing diversity approaches have been applied in several dynamic scenarios. The obtained results from the computational experiments, confirm that self-adaptation is a promising technique for dynamic environments.

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