DocumentCode :
1665608
Title :
An improved FSOA based on stochastic search
Author :
Wang Yong ; Lu Chuang ; Zhang Xin-zheng
Author_Institution :
Coll. of Inf. Sci. & Eng., Guangxi Univ. for Nat., Nanning, China
fYear :
2012
Firstpage :
1601
Lastpage :
1604
Abstract :
In order to overcome the shortcoming of being trapped in local minima in basic FSOA(an optimization approach on using fishing strategy), an improving FSOA is presented based on stochastic search. The improving algorithm makes use of the stochastic searh approach and do not adopt the strategy of constricting search. It shows, from the experimental simulation results of some typical benchmark optimization problems and some typical restricted functions optimization problems, that the proposed optimization algorithm not only has great advantages of convergence property over basic FSOA, PSO and GA, but also can effectively avoid being trapped in local minima.
Keywords :
convergence; optimisation; search problems; stochastic processes; GA; PSO; constricting search; convergence property; fishing strategy; improved FSOA; local minima; optimization algorithm; optimization approach; restricted function optimization problem; stochastic search; stochastic searh approach; Benchmark testing; Educational institutions; Genetic algorithms; Marine animals; Optimization; Particle swarm optimization; Stochastic processes; FSOA; IFSOA; fishing; optimization algorithm; stochastic search;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Automation Robotics & Vision (ICARCV), 2012 12th International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-1-4673-1871-6
Electronic_ISBN :
978-1-4673-1870-9
Type :
conf
DOI :
10.1109/ICARCV.2012.6485386
Filename :
6485386
Link To Document :
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