DocumentCode
2995201
Title
Hybrid evolutionary algorithms based on PSO and GA
Author
Shi, X.H. ; Lu, Y.H. ; Zhou, C.G. ; Lee, H.P. ; Lin, W.Z. ; Liang, Y.C.
Author_Institution
Coll. of Comput. Sci. & Technol., Jilin Univ., Changchun, China
Volume
4
fYear
2003
fDate
8-12 Dec. 2003
Firstpage
2393
Abstract
Inspired by the idea of genetic algorithm, we propose two hybrid evolutionary algorithms based on PSO and GA methods through crossing over the PSO and GA algorithms. The main ideas of the two proposed methods are to integrate PSO and GA methods in parallel and series forms respectively. Simulations for a series of benchmark test functions show that both of the two proposed methods possess better ability to find the global optimum than that of the standard PSO algorithm.
Keywords
artificial intelligence; genetic algorithms; parallel algorithms; artificial intelligence; genetic algorithm; hybrid evolutionary algorithm; parallel algorithm; particle swarm optimization; Algorithm design and analysis; Benchmark testing; Computational modeling; Computer science; Educational institutions; Evolutionary computation; Genetic algorithms; Genetic mutations; High performance computing; Particle swarm optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2003. CEC '03. The 2003 Congress on
Print_ISBN
0-7803-7804-0
Type
conf
DOI
10.1109/CEC.2003.1299387
Filename
1299387
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