DocumentCode :
527494
Title :
A novel hybrid genetic algorithm for global optimization
Author :
Wang, Shuihua ; Wu, Lenan
Author_Institution :
Sch. of Inf. Sci. & Eng., Southeast Univ., Nanjing, China
Volume :
2
fYear :
2010
fDate :
10-12 Aug. 2010
Firstpage :
1058
Lastpage :
1061
Abstract :
In order to propose a more effective function optimization method, a novel algorithm named HGPSA was proposed which integrates the powerful global search ability of GA and the excellent local search ability of PS. The experiments of 10 runs on three test functions (Powell function, Rosenbrock function, and Schaffer function) demonstrate that the proposed algorithm is superior to both GA and PS with respect to the successful rate. Therefore, the proposed algorithm is valid.
Keywords :
genetic algorithms; function optimization; global optimization; hybrid genetic algorithm; local search ability; Computers; Genetic algorithms; Genetics; Microorganisms; Optimization; Search problems; USA Councils; genetic algorithm; global optimization; pattern search;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
Type :
conf
DOI :
10.1109/ICNC.2010.5582983
Filename :
5582983
Link To Document :
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