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A comprehensive review of swarm optimization algorithms.

Mohd Nadhir Ab Wahab1, Samia Nefti-Meziani1, Adham Atyabi2

  • 1Autonomous System and Advanced Robotics Lab, School of Computing, Science and Engineering, University of Salford, Salford, United Kingdom.

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Summary

This survey compares swarm optimization algorithms, finding Differential Evolution (DE) and Particle Swarm Optimization (PSO) most effective for solving complex optimization problems.

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Area of Science:

  • Computational intelligence
  • Optimization algorithms
  • Metaheuristics

Background:

  • Numerous swarm optimization algorithms exist, from early Evolutionary Programming to recent Grey Wolf Optimization.
  • These algorithms show significant potential for addressing diverse optimization challenges.

Purpose of the Study:

  • To conduct an in-depth survey and comparative analysis of prominent swarm optimization algorithms.
  • To evaluate algorithm performance using a suite of benchmark functions and statistical tests.

Main Methods:

  • Explanation and comparison of selected well-known optimization algorithms.
  • Experimental evaluation on thirty benchmark functions.
  • Application of statistical tests to determine significant performance differences.

Main Results:

  • Differential Evolution (DE) demonstrated overall advantage in performance.
  • Particle Swarm Optimization (PSO) closely followed DE in effectiveness.
  • Comparative analysis highlighted the strengths and weaknesses of various approaches.

Conclusions:

  • DE and PSO emerge as highly effective methods for optimization tasks.
  • The study provides valuable insights into the comparative performance of swarm intelligence algorithms.