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

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Abstract

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.

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Authors and Affiliations

  1. Department of Computer Science, Shirvan Branch, Islamic Azad University, Shirvan, Iran

    Mehdi Neshat

  2. Department of Computer Engineering, Shirvan Branch, Islamic Azad University, Shirvan, Iran

    Ghodrat Sepidnam & Mehdi Sargolzaei

  3. Department of Computer Science and Software Engineering, Shirvan Branch, Islamic Azad University, Shirvan, Iran

    Adel Najaran Toosi

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  1. Mehdi Neshat
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  2. Ghodrat Sepidnam
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  3. Mehdi Sargolzaei
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  4. Adel Najaran Toosi
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Correspondence to Mehdi Neshat.

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Neshat, M., Sepidnam, G., Sargolzaei, M. et al. Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif Intell Rev 42, 965–997 (2014). https://doi.org/10.1007/s10462-012-9342-2

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