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Optimización adaptativa diferenciada del loro: un algoritmo mejorado de múltiples estrategias para la optimización

Guanjun Lin1, Mahmoud Abdel-Salam2, Gang Hu3

  • 1School of Information Engineering, Sanming University, Sanming 365004, China.

Biomimetics (Basel, Switzerland)
|August 27, 2025
PubMed
Resumen
Este resumen es generado por máquina.

El algoritmo de optimización de loro diferenciado adaptativo (ADPO) mejora el algoritmo de optimización de loro original (PO) al mejorar la diversidad de la población y la efectividad de la búsqueda. ADPO demuestra un rendimiento superior en tareas complejas de optimización y pronóstico de energía eólica.

Palabras clave:
Las LTMCazas basadas en el aprendizaje de dimensionesPronósticos energéticosAlgoritmo de optimización del loro

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Área de la Ciencia:

  • Inteligencia computacional
  • Optimización metaheurística
  • Algoritmos inspirados en la naturaleza

Sus antecedentes:

  • El algoritmo de optimización del loro (PO) es una metaheurística inspirada en la naturaleza basada en el comportamiento del loro.
  • PO se enfrenta a desafíos con la diversidad de la población y la convergencia temprana en problemas de optimización complejos.
  • Los algoritmos existentes luchan por mantener la efectividad de la búsqueda e identificar soluciones óptimas.

Objetivo del estudio:

  • Introducir el algoritmo de optimización de loros diferenciados adaptativos (ADPO) para superar las limitaciones de PO.
  • Mejorar las capacidades de exploración, explotación y convergencia del algoritmo de optimización de Parrot.
  • Validar la eficacia de ADPO en las funciones de referencia y su aplicación en el mundo real.

Principales métodos:

  • Desarrolló ADPO con tres mecanismos novedosos: Variación Diferencial Media (MDV), Caza Basada en el Aprendizaje de Dimensiones (DLH) y Mutualismo Adaptativo Mejorado (EAM).
  • MDV emplea mutación de doble fase para una exploración y explotación equilibradas.
  • DLH utiliza el aprendizaje dimensional para evitar la convergencia prematura y mantener la diversidad.
  • El EAM introduce interacciones guiadas por la aptitud para una intensificación y diversificación equilibradas.

Principales resultados:

  • ADPO demostró una velocidad de convergencia superior, eficiencia de búsqueda y precisión de solución en las funciones de referencia CEC2017 y CEC2022.
  • En el pronóstico de la energía eólica con LSTM, ADPO logró un R2 promedio de 0,9726, superando a los métodos convencionales.
  • ADPO logró consistentemente clasificaciones superiores de Friedman (1.42-2.78) contra 12 algoritmos avanzados.

Conclusiones:

  • El ADPO propuesto mejora significativamente las capacidades de optimización en comparación con el PO de referencia.
  • ADPO muestra un rendimiento y una eficacia sólidos en la optimización compleja y la predicción de la energía renovable.
  • Los nuevos mecanismos abordan efectivamente las cuestiones de diversidad y convergencia de la población en la búsqueda metaheurística.