Fuzzy adaptive artificial fish swarm algorithm

Danial Yazdani, Adel Nadjaran Toosi, Mohammad Reza Meybodi

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Artificial Fish Swarm Algorithm (AFSA) is a kind of swarm intelligence algorithms which is usually employed in optimization problems. There are many parameters to adjust in AFSA like visual and step. Through constant initializing of visual and step parameters, algorithm is only able to do local searching or global searching. In this paper, two new adaptive methods based on fuzzy systems are proposed to control the visual and step parameters during the AFSA execution in order to control the capability of global and local searching adaptively. First method uniformly adjusts the visual and step of all fish whereas in the second method, each artificial fish has its own fuzzy controller for adjusting its visual and step parameters. Evaluations of the proposed methods were performed on eight well known benchmark functions in comparison with standard AFSA and Particle Swarm Optimization (PSO). The overall results show that proposed algorithm can be effective surprisingly.

Original languageEnglish
Title of host publicationAI 2010
Subtitle of host publicationAdvances in Artificial Intelligence - 23rd Australasian Joint Conference, Proceedings
PublisherSpringer
Pages334-343
Number of pages10
ISBN (Print)3642174310, 9783642174315
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event23rd Australasian Joint Conference on Artificial Intelligence, AI 2010 - Adelaide, SA, Australia
Duration: 7 Dec 201010 Dec 2010

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume6464
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd Australasian Joint Conference on Artificial Intelligence, AI 2010
CountryAustralia
CityAdelaide, SA
Period7/12/1010/12/10

Keywords

  • Artificial Fish Swarm Algorithm (AFSA)
  • fuzzy system
  • global search
  • local search
  • particle Swarm Optimization (PSO)

Cite this

Yazdani, D., Nadjaran Toosi, A., & Meybodi, M. R. (2010). Fuzzy adaptive artificial fish swarm algorithm. In AI 2010: Advances in Artificial Intelligence - 23rd Australasian Joint Conference, Proceedings (pp. 334-343). (Lecture Notes in Computer Science ; Vol. 6464 ). Springer. https://doi.org/10.1007/978-3-642-17432-2_34
Yazdani, Danial ; Nadjaran Toosi, Adel ; Meybodi, Mohammad Reza. / Fuzzy adaptive artificial fish swarm algorithm. AI 2010: Advances in Artificial Intelligence - 23rd Australasian Joint Conference, Proceedings. Springer, 2010. pp. 334-343 (Lecture Notes in Computer Science ).
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Yazdani, D, Nadjaran Toosi, A & Meybodi, MR 2010, Fuzzy adaptive artificial fish swarm algorithm. in AI 2010: Advances in Artificial Intelligence - 23rd Australasian Joint Conference, Proceedings. Lecture Notes in Computer Science , vol. 6464 , Springer, pp. 334-343, 23rd Australasian Joint Conference on Artificial Intelligence, AI 2010, Adelaide, SA, Australia, 7/12/10. https://doi.org/10.1007/978-3-642-17432-2_34

Fuzzy adaptive artificial fish swarm algorithm. / Yazdani, Danial; Nadjaran Toosi, Adel; Meybodi, Mohammad Reza.

AI 2010: Advances in Artificial Intelligence - 23rd Australasian Joint Conference, Proceedings. Springer, 2010. p. 334-343 (Lecture Notes in Computer Science ; Vol. 6464 ).

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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AB - Artificial Fish Swarm Algorithm (AFSA) is a kind of swarm intelligence algorithms which is usually employed in optimization problems. There are many parameters to adjust in AFSA like visual and step. Through constant initializing of visual and step parameters, algorithm is only able to do local searching or global searching. In this paper, two new adaptive methods based on fuzzy systems are proposed to control the visual and step parameters during the AFSA execution in order to control the capability of global and local searching adaptively. First method uniformly adjusts the visual and step of all fish whereas in the second method, each artificial fish has its own fuzzy controller for adjusting its visual and step parameters. Evaluations of the proposed methods were performed on eight well known benchmark functions in comparison with standard AFSA and Particle Swarm Optimization (PSO). The overall results show that proposed algorithm can be effective surprisingly.

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Yazdani D, Nadjaran Toosi A, Meybodi MR. Fuzzy adaptive artificial fish swarm algorithm. In AI 2010: Advances in Artificial Intelligence - 23rd Australasian Joint Conference, Proceedings. Springer. 2010. p. 334-343. (Lecture Notes in Computer Science ). https://doi.org/10.1007/978-3-642-17432-2_34