DocumentCode :
1795258
Title :
Improved immune algorithm based on a global strategy
Author :
Xian Yong Jing ; Man Yi Hou ; Wei Peng Wang ; Cheng Da Ning ; Tian Zhao
Author_Institution :
Campaign & Command Dept., Aviation Univ. of Air Force, Changchun, China
fYear :
2014
fDate :
8-10 Aug. 2014
Firstpage :
2140
Lastpage :
2143
Abstract :
Imitating the antibody diversity maintaining mechanism of immune system to realize the global optimization is a target that the immune algorithm try to achieve. Based on the in-depth study of inhibition concentration mechanism, the global optimization characteristic of existing immune algorithm is analyzed, then a global conservation strategy for colony is proposed. Based on the strategy, the improved algorithm is of more outstanding global and fast convergence ability. Simulation is implemented based on Matlab, the algorithm is applied to train a neural network prediction model and it is compared with an existing typical immune algorithm. Simulation results show that the immune algorithm improved by the strategy in this paper is better than the previous algorithms in global evolution, fast convergence and other key indicators.
Keywords :
artificial immune systems; Matlab; convergence ability; global optimization; immune algorithm; inhibition concentration mechanism; neural network prediction model; Algorithm design and analysis; Convergence; Immune system; Optimization; Prediction algorithms; Predictive models; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Guidance, Navigation and Control Conference (CGNCC), 2014 IEEE Chinese
Conference_Location :
Yantai
Print_ISBN :
978-1-4799-4700-3
Type :
conf
DOI :
10.1109/CGNCC.2014.7007505
Filename :
7007505
Link To Document :
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