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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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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
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Parallel sub class modified teaching learning based optimization.

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
Summary
This summary is machine-generated.

This study introduces Parallel Sub-Class Modified Teaching-Learning-Based Optimization (PSC-MTLBO), an enhanced algorithm for complex optimization problems. PSC-MTLBO significantly improves search efficiency and solution accuracy, outperforming existing meta-heuristic methods.

Keywords:
Benchmark functionsCEC2005CEC2014Friedman rankMeta-heuristicOptimizationTruss topology optimization

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Engineering Design

Background:

  • Meta-heuristic algorithms require balancing exploration and exploitation to avoid premature convergence.
  • Existing Teaching-Learning-Based Optimization (TLBO) variants need further enhancement for superior performance.

Purpose of the Study:

  • To propose and evaluate the Parallel Sub-Class Modified Teaching-Learning-Based Optimization (PSC-MTLBO) algorithm.
  • To enhance search efficiency, solution accuracy, and convergence speed in optimization problems.

Main Methods:

  • Integrated adaptive teaching factors, tutorial-based learning, and self-motivated learning.
  • Introduced novel sub-class division and challenger learners' models.
  • Validated on benchmark functions (CEC2005, CEC2014) and truss topology optimization problems.

Main Results:

  • PSC-MTLBO demonstrated superior performance over TLBO, MTLBO, PSO, DE, and GWO.
  • Achieved maximum overall rank in 80% of test functions, reducing function errors by up to 95% compared to traditional TLBO.
  • Designed lighter, more cost-effective truss structures with a 7.2% weight reduction.

Conclusions:

  • PSC-MTLBO offers a highly efficient and scalable optimization framework.
  • The novel strategies enhance adaptability, convergence, and result stability.
  • PSC-MTLBO shows significant advantages for solving complex optimization challenges.