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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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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
PubMed
Resumen
Este resumen es generado por máquina.

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.

Palabras clave:
Funciones de referenciaLa Comunidad Europea 2005La CEC2014El rango de FriedmanMeta-heurísticaOptimizaciónOptimización de la topología de truss

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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.