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Related Concept Videos

Observational Learning01:12

Observational Learning

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 because...

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Operant Learning of Drosophila at the Torque Meter
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An improved sand cat swarm optimization with lens opposition-based learning and sparrow search algorithm.

Yanguang Cai1,2, Changle Guo3, Xiang Chen1

  • 1School of Automation, Guangdong University of Technology, Guangzhou, 511400, China.

Scientific Reports
|September 5, 2024
PubMed
Summary

The improved sand cat swarm optimization (LSSCSO) enhances global search capabilities. This novel algorithm effectively avoids local optima, improving optimization performance for complex problems.

Keywords:
Engineering optimization problemsLens opposition-based learningSand cat swarm optimizationSparrow search algorithm

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Meta-heuristics

Background:

  • The Sand Cat Swarm Optimization (SCSO) algorithm, inspired by feline hunting behavior, faces challenges with local optima in later stages.
  • Existing meta-heuristic algorithms require improvements in global search and convergence accuracy.

Purpose of the Study:

  • To introduce an improved algorithm, LSSCSO, addressing the limitations of SCSO.
  • To enhance the global search capability and convergence accuracy of the Sand Cat Swarm Optimization algorithm.

Main Methods:

  • Developed LSSCSO by integrating dynamic spiral search, lens opposition-based learning, and the Sparrow Search Algorithm into SCSO.
  • Validated LSSCSO performance using CEC2005 and CEC2022 test functions across various dimensions.
  • Applied LSSCSO to engineering optimization problems to assess practical effectiveness.

Main Results:

  • LSSCSO demonstrated superior optimization ability compared to standard SCSO.
  • The algorithm effectively avoided local optima and improved convergence accuracy.
  • Statistical analysis (Wilcoxon rank sum test) confirmed LSSCSO's robust performance.

Conclusions:

  • LSSCSO significantly enhances the global search capability and convergence accuracy of the Sand Cat Swarm Optimization algorithm.
  • The proposed LSSCSO algorithm shows strong potential for solving complex global and engineering optimization problems effectively.