DocumentCode :
2876119
Title :
An Improved Artificial Fish Swarm Algorithm Based on Chaotic Search and Feedback Strategy
Author :
Zhu, Kongcun ; Jiang, Mingyan
Author_Institution :
Sch. of Inf. Sci. & Eng., Shandong Univ., Jinan, China
fYear :
2009
fDate :
11-13 Dec. 2009
Firstpage :
1
Lastpage :
4
Abstract :
Artificial fish swarm algorithm (AFSA) is a kind of swarm intelligence algorithms, which has the features of not strict to parameter setting, insensitive to initial values, strong robustness and so on. But the precision can not be very high and artificial fish (AF) often suffers the problem of being trapped in local optima. Especially when the objective function is a multimodel function, this problem is more prominent. Since chaotic mapping enjoys certainty, ergodicity and stochastic property, chaotic search can serve as a kind of method for global optimization. Feedback can also act as a strategy to lead the movement of AF. In this paper, chaotic search and feedback strategy are introduced into AFSA to overcome the shortcoming above. The experimental results show that the improved AFSA can obtain better results than the standard AFSA.
Keywords :
feedback; optimisation; chaotic search; feedback strategy; global optimization; improved artificial fish swarm algorithm; Artificial intelligence; Chaos; Clustering algorithms; Convergence; Feedback; Information science; Marine animals; Particle swarm optimization; Robustness; Stochastic processes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4507-3
Electronic_ISBN :
978-1-4244-4507-3
Type :
conf
DOI :
10.1109/CISE.2009.5366958
Filename :
5366958
Link To Document :
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