Improved immune algorithm based on a global strategy
作者： Xian Yong. Jing , Man Yi. Hou , Wei Peng. Wang , Cheng Da Ning , Tian. Zhao
DOI: 10.1109/CGNCC.2014.7007505
关键词:
摘要: Imitating the antibody diversity maintaining mechanism of immune system to realize global optimization is a target that algorithm try achieve. Based on in-depth study inhibition concentration mechanism, characteristic existing analyzed, then conservation strategy for colony proposed. strategy, improved more outstanding and fast convergence ability. Simulation implemented based Matlab, applied train neural network prediction model it compared with an typical algorithm. results show by in this paper better than previous algorithms evolution, other key indicators.
07007505.pdf 参考文章(4)
Gang Lu, De-jian Tan, He-ming Zhao, Improvement on regulating definition of antibody density of immune algorithm international conference on neural information processing. ,vol. 5, pp. 2669- 2672 ,(2002) , 10.1109/ICONIP.2002.1201980
Guo Zilong, Wang Sun’an, Zhuang Jian, A novel immune evolutionary algorithm incorporating chaos optimization Pattern Recognition Letters. ,vol. 27, pp. 2- 8 ,(2006) , 10.1016/J.PATREC.2005.06.014
Hsu-Hao Yang, Yen-Liang Chen, Finding K shortest looping paths with waiting time in a time–window network Applied Mathematical Modelling. ,vol. 30, pp. 458- 465 ,(2006) , 10.1016/J.APM.2005.05.005
Zhong Wei, A Novel Clustering Based on the Immune Evolutionary Algorithm Acta Electronica Sinica. ,(2001)