Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications

Mehdi Neshat, Ghodrat Sepidnam, Mehdi Sargolzaei, Adel Najaran Toosi

Research output: Contribution to journalArticleResearchpeer-review

178 Citations (Scopus)


AFSA (artificial fish-swarm algorithm) is one of the best methods of optimization among the swarm intelligence algorithms. This algorithm is inspired by the collective movement of the fish and their various social behaviors. Based on a series of instinctive behaviors, the fish always try to maintain their colonies and accordingly demonstrate intelligent behaviors. Searching for food, immigration and dealing with dangers all happen in a social form and interactions between all fish in a group will result in an intelligent social behavior.This algorithm has many advantages including high convergence speed, flexibility, fault tolerance and high accuracy. This paper is a review of AFSA algorithm and describes the evolution of this algorithm along with all improvements, its combination with various methods as well as its applications. There are many optimization methods which have a affinity with this method and the result of this combination will improve the performance of this method. Its disadvantages include high time complexity, lack of balance between global and local search, in addition to lack of benefiting from the experiences of group members for the next movements.

Original languageEnglish
Pages (from-to)965-997
Number of pages33
JournalArtificial Intelligence Review
Issue number4
Publication statusPublished - 1 Dec 2014
Externally publishedYes


  • Artificial fish swarm optimization
  • Natural computing
  • Swarm optimization

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