DocumentCode :
2373696
Title :
An environment-gene double evolution immune clone algorithm for constrained optimization
Author :
Xia, Hu ; Zhuang, Jian ; Yu, Dehong
Author_Institution :
Sch. of Mech. Eng., Xi´´an Jiaotong Univ., Xi´´an, China
fYear :
2010
fDate :
4-7 Aug. 2010
Firstpage :
1495
Lastpage :
1500
Abstract :
By referencing the effect of environment to the biologic evolution, an environment-gene double evolution immune clone algorithm (EGICA) is proposed based on normal immune clone algorithm. This algorithm can avoid blind search effectively and enhance the convergence speed since an environment-gene double evolution mutation operator is introduced, which can accumulate the experience of evolution process. In other words, it means that EGICA has self-learning capability. Moreover, a new sequencing strategy is used for design cost function to solve constrained Optimization. Then, the convergence of EGICA is proved with probability 1. At last, by the experiments of testing 13 classical benchmarks of constraint optimal problem, it shows that EGICA has good capability.
Keywords :
evolutionary computation; nonlinear programming; biologic evolution; constrained optimization; environment-gene double evolution immune clone algorithm; environment-gene double evolution mutation operator; nonlinear programming; normal immune clone algorithm; Algorithm design and analysis; Cloning; Cost function; Evolution (biology); Immune system; Strontium;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Mechatronics and Automation (ICMA), 2010 International Conference on
Conference_Location :
Xi´an
ISSN :
2152-7431
Print_ISBN :
978-1-4244-5140-1
Electronic_ISBN :
2152-7431
Type :
conf
DOI :
10.1109/ICMA.2010.5589253
Filename :
5589253
Link To Document :
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