DocumentCode :
3314257
Title :
Self-Adapting Chaos-Genetic Hybrid Algorithm with Mixed Congruential Method
Author :
Bing-rui, Chen ; Xia-ting, Feng
Author_Institution :
State Key Lab. of Geomechanics & Geotechnical Eng., Chinese Acad. of Sci., Wuhan
Volume :
7
fYear :
2008
fDate :
18-20 Oct. 2008
Firstpage :
674
Lastpage :
677
Abstract :
An improved swarm intelligence algorithm, named SA-CGA, is introduced briefly in the paper. The algorithm, which is a chaos-genetic hybrid algorithm with a new random number generator using the mixed congruential method, searches goal value using genetic algorithm in global space when population diversity is bigger than given value, while resolves optimal value utilizing chaos algorithm as population diversity decreases to some threshold automatically. Uncertainty of solution is solved well with the mixed congruential method. The performance of the algorithms is analyzed and compared with other methods. The result shows its convergence precision is high and its convergence velocity is fast.
Keywords :
chaos; genetic algorithms; random number generation; SA-CGA; convergence velocity; genetic algorithm; improved swarm intelligence algorithm; mixed congruential method; population diversity; random number generator; self-adapting chaos-genetic hybrid algorithm; Algorithm design and analysis; Chaos; Convergence; Genetic algorithms; Laboratories; Particle swarm optimization; Performance analysis; Random number generation; Runtime; Soil; Mixed Congruential Method; Self-adapting; chaos optimization; genetic algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation, 2008. ICNC '08. Fourth International Conference on
Conference_Location :
Jinan
Print_ISBN :
978-0-7695-3304-9
Type :
conf
DOI :
10.1109/ICNC.2008.116
Filename :
4668061
Link To Document :
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