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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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An Enhanced Team-Oriented Swarm Optimization Algorithm (ETOSO) for Robust and Efficient High-Dimensional Search.

Adel BenAbdennour1

  • 1College of Engineering, Islamic University of Madinah, Madinah 42351, Saudi Arabia.

Biomimetics (Basel, Switzerland)
|April 25, 2025
PubMed
Summary

The Enhanced Team-Oriented Swarm Optimization (ETOSO) algorithm overcomes stagnation in nature-inspired methods. ETOSO shows superior performance in complex, high-dimensional optimization problems.

Keywords:
benchmarksenhanced team-oriented swarm optimizationexploration and exploitationhigh-dimensional search spacesnature-inspired algorithmsoptimization algorithms

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Nature-Inspired Computing

Background:

  • Nature-inspired optimization algorithms (NIOAs) often suffer from stagnation.
  • Existing algorithms like Team-Oriented Swarm Optimization (TOSO) require refinement to improve performance.
  • Addressing stagnation is crucial for enhancing solution accuracy and convergence speed.

Purpose of the Study:

  • Introduce the Enhanced Team-Oriented Swarm Optimization (ETOSO) algorithm.
  • Improve upon the TOSO algorithm by integrating novel exploration and exploitation strategies.
  • Evaluate ETOSO's effectiveness in handling high-dimensional search spaces and mitigating stagnation.

Main Methods:

  • Developed the ETOSO algorithm with enhanced exploration and exploitation mechanisms.
  • Conducted comparative evaluations against 26 established NIOAs.
  • Utilized 15 benchmark functions across various dimensions (D = 2 to 200) for rigorous testing.

Main Results:

  • ETOSO demonstrated superior performance in solution accuracy and convergence speed.
  • The algorithm showed improved computational complexity and consistency compared to other NIOAs.
  • ETOSO effectively addressed the stagnation problem in high-dimensional optimization tasks.

Conclusions:

  • ETOSO is a robust and efficient refinement of TOSO for nature-inspired optimization.
  • The algorithm offers a simplified yet powerful approach to complex optimization challenges.
  • ETOSO represents a significant advancement in tackling stagnation in high-dimensional search spaces.