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Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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Un enfoque heurístico híbrido auxiliar para la evaluación del diseño de la función objetiva, utilizando la

Li Lei1, Raymond Kwan1, Zhiyuan Lin2

  • 1School of Computing, University of Leeds, Woodhouse Lane, Leeds, LS2 9JT West Yorkshire UK.

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Resumen

Este estudio introduce un método para evaluar funciones objetivas para la solución de problemas de optimización complejos heurísticos híbridos. Las funciones objetivas efectivas mejoran significativamente la calidad y la diferenciación de las soluciones en las aplicaciones prácticas.

Palabras clave:
Proceso de jerarquía analíticaOptimización combinatoriaHeurísticas híbridasDiseño de la función objetivoEvaluación de la función objetivoProgramación de las unidades ferroviarias

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Área de la Ciencia:

  • Investigación de las operaciones
  • Ciencias de la computación
  • Matemáticas aplicadas

Sus antecedentes:

  • Los problemas de optimización del mundo real a menudo son NP-difícil, lo que requiere soluciones casi óptimas.
  • La diferenciación entre soluciones casi óptimas es crucial para las aplicaciones prácticas.
  • Incorporar numerosas propiedades estructurales en funciones objetivas es un desafío.

Objetivo del estudio:

  • Proponer y demostrar una metodología para la evaluación comparativa de diseños de funciones objetivas en heurística híbrida.
  • Evaluar la eficacia de las funciones objetivas para diferenciar la calidad de la solución y acelerar el proceso de solución.
  • Explorar la satisfacción implícita de los criterios no modelados a través de un diseño objetivo efectivo.

Principales métodos:

  • Utilizando heurísticas híbridas (meta) que emplean iterativamente un solucionador exacto en instancias de problemas reducidos.
  • Desarrollar una metodología de evaluación comparativa con similitud estructural a las soluciones exactas como métrica primaria.
  • Agregación de otras características de la solución y evaluación de funciones objetivo alternativas en un problema de programación de la unidad de tren.

Principales resultados:

  • Dos de las cuatro funciones objetivas probadas resultaron significativamente más eficaces que otras.
  • Las funciones objetivas efectivas mejoraron la diferenciación de la solución y aceleraron el proceso de solución.
  • Se observó la satisfacción implícita de ciertos criterios con funciones objetivas bien diseñadas.

Conclusiones:

  • La metodología de evaluación comparativa propuesta evalúa efectivamente los diseños de funciones objetivas para la heurística híbrida.
  • El diseño de la función objetiva es fundamental para evitar espacios de solución mal diferenciados.
  • Las funciones objetivas efectivas pueden conducir a soluciones de mejor calidad y a la satisfacción implícita de las limitaciones prácticas.