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Adaptación de MOEA/D a CMA-ES para abordar problemas multiobjetivo mal condicionados

Chengyu Lu1, Zhenhua Li2, Qingfu Zhang3

  • 1Department of Computer Science, City University of Hong Kong, Hong Kong, China chengyulu3-c@my.cityu.edu.hk.

Evolutionary computation
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Resumen

Un nuevo algoritmo, MOES/D, aborda problemas mal condicionados en la optimización multiobjetivo evolutiva. Resuelve de manera eficiente problemas no separables y mal condicionados, superando a los métodos existentes.

Palabras clave:
estrategia evolutivacolaboracióndescomposiciónmal condicionamientooptimización multiobjetivono separabilidadasignación de recursos

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Área de la Ciencia:

  • Computación Evolutiva
  • Optimización Multiobjetivo
  • Diseño de Algoritmos

Sus antecedentes:

  • Los problemas mal condicionados plantean desafíos significativos en la optimización mono-objetivo.
  • Estos desafíos no se abordan en gran medida en la optimización multiobjetivo evolutiva (EMO).
  • Los enfoques EMO existentes pueden comprometer las características centrales de la estrategia evolutiva al integrarlos.

Objetivo del estudio:

  • Introducir una nueva estrategia de evolución multiobjetivo basada en la descomposición (MOES/D).
  • Abordar problemas de optimización multiobjetivo no separables y mal condicionados.
  • Desarrollar estrategias personalizadas para coordinar algoritmos evolutivos.

Principales métodos:

  • Se desarrolló MOES/D, una estrategia de evolución multiobjetivo basada en la descomposición.
  • Se implementó un algoritmo de mezcla de importancia para una eficiencia de muestra imparcial.
  • Se utilizó un método de ascenso colaborativo para la optimización simultánea de subproblemas.
  • Se aplicó la expectativa-maximización para la asignación de recursos basada en principios para priorizar modelos.

Principales resultados:

  • MOES/D demuestra un rendimiento superior en problemas multiobjetivo moderada y mal condicionados.
  • El algoritmo supera significativamente a la mayoría de los algoritmos del estado del arte.
  • Se realizaron experimentos en un nuevo conjunto de pruebas de problemas no separables y mal condicionados.

Conclusiones:

  • MOES/D resuelve eficazmente problemas multiobjetivo no separables y mal condicionados desafiantes.
  • Las estrategias personalizadas propuestas mejoran la eficiencia y las capacidades de los algoritmos evolutivos en EMO.
  • Este trabajo cierra una brecha crítica en la investigación EMO al abordar instancias mal condicionados.