A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms

Han Duy Phan, Kirsten Ellis, Jan Carlo Barca, Alan Dorin

Research output: Contribution to journalReview ArticleResearchpeer-review

30 Citations (Scopus)


Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for evolutionary algorithms. Counterparts providing an overview of parameter setting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parameter setting approaches that have been applied to them. This review simplifies the selection of parameter setting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parameter setting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms.

Original languageEnglish
Pages (from-to)567-588
Number of pages22
JournalNeural Computing and Applications
Issue number2
Publication statusPublished - Jan 2020


  • Dynamic parameter setting
  • Parameter control
  • Swarm intelligence algorithms

Cite this