Particle Swarm Optimization: A Comprehensive Survey

Tareq M. Shami, Ayman A. El-Saleh, Mohammed Alswaitti, Qasem Al-Tashi, Mhd Amen Summakieh, Seyedali Mirjalili

Research output: Contribution to journalArticlepeer-review

183 Citations (Scopus)


Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

Original languageEnglish
JournalIEEE Access
Publication statusPublished - 2022


  • Applications of PSO
  • Binary PSO
  • Birds
  • evolutionary computation
  • Feature extraction
  • feature selection
  • hybrid algorithms
  • meta-heuristic algorithms
  • Optimization
  • Particle swarm optimization
  • Particle Swarm Optimization
  • PSO variants
  • Signal processing algorithms
  • Statistics
  • Topology


Dive into the research topics of 'Particle Swarm Optimization: A Comprehensive Survey'. Together they form a unique fingerprint.

Cite this