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Optimización basada en el aprendizaje de la enseñanza modificada de la subclase paralela
Ghanshyam G Tejani1,2, Sunil Kumar Sharma3, Shailendra Mishra4
1Department of Research Analytics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, 600077, India. gtejani@saturn.yzu.edu.tw.
Scientific reports
|August 29, 2025
Resumen
Este estudio presenta la optimización basada en la enseñanza-aprendizaje modificada de subclases paralelas (PSC-MTLBO), un algoritmo mejorado para problemas de optimización complejos. PSC-MTLBO mejora significativamente la eficiencia de búsqueda y la precisión de la solución, superando a los métodos meta-heurísticos existentes.
Área de la Ciencia:
- Inteligencia computacional
- Algoritmos de optimización
- Diseño de ingeniería
Sus antecedentes:
- Los algoritmos meta-heurísticos requieren equilibrar la exploración y la explotación para evitar una convergencia prematura.
- Las variantes existentes de optimización basada en la enseñanza y el aprendizaje (TLBO) necesitan una mejora adicional para un rendimiento superior.
Objetivo del estudio:
- Proponer y evaluar el algoritmo de optimización basada en la enseñanza-aprendizaje modificada paralela de subclases (PSC-MTLBO).
- Para mejorar la eficiencia de búsqueda, la precisión de la solución y la velocidad de convergencia en problemas de optimización.
Principales métodos:
- Factores de enseñanza adaptativa integrada, aprendizaje basado en tutoriales y aprendizaje auto-motivado.
- Se introdujeron nuevos modelos de división de subclases y de aprendices desafiantes.
- Validado en funciones de referencia (CEC2005, CEC2014) y problemas de optimización de la topología de la armadura.
Principales resultados:
- El PSC-MTLBO demostró un rendimiento superior al TLBO, MTLBO, PSO, DE y GWO.
- Alcanzó el máximo rango general en el 80% de las funciones de prueba, reduciendo los errores de función hasta en un 95% en comparación con el TLBO tradicional.
- Se han diseñado estructuras más ligeras y rentables con una reducción de peso del 7,2%.
Conclusiones:
- PSC-MTLBO ofrece un marco de optimización altamente eficiente y escalable.
- Las nuevas estrategias mejoran la adaptabilidad, la convergencia y la estabilidad de los resultados.
- PSC-MTLBO muestra ventajas significativas para resolver desafíos de optimización complejos.
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