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Particle Swarm Optimisation: A Historical Review Up to the Current Developments.

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Particle Swarm Optimisation (PSO) is a popular algorithm facing convergence issues. This review details numerous variants and improvements to enhance PSO performance for diverse applications.

Keywords:
Particle Swarm Optimisation (PSO)bio-inspired algorithmscomputational intelligenceoptimisationstochastic algorithmsswarm intelligence

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • Particle Swarm Optimisation (PSO) is a metaheuristic technique inspired by bird flocking behavior.
  • PSO is widely used due to its simplicity and broad applicability in various optimization problems.
  • Original PSO suffers from convergence and performance issues, necessitating algorithmic improvements.

Purpose of the Study:

  • To conduct a systematic literature review of PSO variants and enhancements.
  • To cover hybridisation, parallelisation, and extensions of PSO to different optimization problem classes.
  • To facilitate the selection of appropriate PSO variants for specific applications.

Main Methods:

  • Systematic literature review of PSO variants and improvements.
  • Categorisation and summarisation of proposed approaches.
  • Analysis of hybridisation, parallelisation, and extensions.

Main Results:

  • A comprehensive overview of numerous PSO variants and enhancements.
  • Identification of strategies to address original PSO limitations.
  • Classification of improvements for different optimization problem types.

Conclusions:

  • Significant advancements have been made to the original PSO algorithm.
  • Various PSO modifications offer improved performance and convergence.
  • This review provides a structured guide for selecting optimal PSO variants.