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Un algoritmo genético cuántico mejorado con rotación dinámica adaptativa para la optimización espacial de

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Este resumen es generado por máquina.

Un algoritmo genético cuántico mejorado (IQGA-ADA) mejora la asignación de instalaciones en la planificación urbana. Este método supera las limitaciones de los algoritmos tradicionales, mejorando la optimización espacial y la equidad en la ubicación de las instalaciones.

Palabras clave:
ángulo de rotación dinámicoalgoritmo genéticotecnología multimediaoptimización espacialplanificación urbana

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Área de la Ciencia:

  • Planificación y Diseño Urbano
  • Inteligencia Computacional
  • Optimización Geoespacial

Sus antecedentes:

  • La asignación de instalaciones es fundamental para la planificación urbana, pero presenta complejos desafíos de optimización.
  • Los algoritmos genéticos existentes luchan con la convergencia y la diversidad en problemas espaciales a gran escala.
  • Los entornos urbanos presentan restricciones como la correlación geográfica y la heterogeneidad.

Objetivo del estudio:

  • Proponer un algoritmo genético cuántico mejorado con ángulo de rotación dinámico adaptativo (IQGA-ADA) para la configuración espacial de instalaciones.
  • Abordar la convergencia prematura y la limitada diversidad de búsqueda en los métodos heurísticos existentes.
  • Mejorar la efectividad y la equidad de los esquemas de planificación de instalaciones urbanas.

Principales métodos:

  • Se desarrolló IQGA-ADA con un mecanismo de ángulo de rotación dinámico adaptativo para la renovación de la población.
  • Se integró la S-transform para procesar datos espaciales multimodales (población, tráfico, ubicaciones de instalaciones).
  • Se extrajeron cantidades características (raíz cuadrática media, valor medio) para la identificación de patrones.

Principales resultados:

  • IQGA-ADA demostró una mejora del 68% en el valor de aptitud en comparación con el algoritmo genético de codificación real (RCGA).
  • Logró una convergencia más rápida (aproximadamente 5 iteraciones) frente a RCGA (125 iteraciones).
  • Mostró capacidades de búsqueda global mejoradas y rendimiento en la optimización espacial de alta dimensionalidad.

Conclusiones:

  • IQGA-ADA supera eficazmente las limitaciones de los algoritmos tradicionales para la optimización espacial de instalaciones.
  • El algoritmo mejora la equidad en la reposición de instalaciones, como se demostró en un estudio de caso de instalaciones de primeros auxilios.
  • Los mecanismos de evolución cuántica muestran un potencial significativo para resolver problemas complejos de optimización geoespacial.