A symbiosis-based artificial fish swarm algorithm

Author

Qing Liu ; Odaka, Toshiyuki ; Kuroiwa, Jousuke ; Shirai, Hiroshi ; Ogura, Hisakazu

Author_Institution

Dept. of Nucl. Power & Energy Safety Eng., Univ. of Fukui Fukui, Fukui, Japan

fYear

2013

fDate

23-25 July 2013

Firstpage

379

Lastpage

385

Abstract

This paper presents a symbiosis-based artificial fish swarm algorithm (AFSA), which employs two artificial fish (AF) populations to collaboratively search the solution space of the problem to be solved. The symbiosis-based framework makes AF individuals adaptively adjust their Visual-limit and Step-limit. Due to the irrationality that AF individual swims toward its partners´ geometric centre when gathering, a weighted centre and the method for calculating it was proposed. Beyond this, this paper also presents a newly designed behavioral strategy for AF. Experiments were conducted on several test functions. The results demonstrate good performance of the symbiosis-based AFSA when compared with several other swarm intelligence-based algorithms.

Keywords

optimisation; search problems; swarm intelligence; AFSA; artificial fish populations; behavioral strategy; geometric centre; solution space search; step-limit adjustment; swarm intelligence-based optimization tool; symbiosis-based artificial fish swarm algorithm; symbiosis-based framework; visual-limit adjustment; weighted centre; Convergence; Marine animals; Optimization; Search problems; Sociology; Statistics; Visualization; artificial fish swarm algorithm; host population; optimization; symbiont population; symbiosis;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2013 Ninth International Conference on

Conference_Location

Shenyang

Type

conf

DOI

10.1109/ICNC.2013.6818005

Filename

6818005