Mingfeng Zhu
School of Computer Science, Jiangxi University of Traditional Chinese Medicine, Nanchang, 330004, China
ABSTRACT
Aiming at the shortcomings of low accuracy and low efficiency of the conventional genetic algorithm, an improved chaos genetic algorithm is proposed to solve the problem of mixed batch blending. Firstly, turbulence of chaos is adopted to produce initial population. Secondly, a real number coding space is taken as genotype of the problem in which the crossover and mutation operations are carried out. Thirdly, after the crossover, mutation and selection operations are performed, turbulence of chaos is once again applied to those individuals with relatively low fitness so as to accelerate the speed of convergence. In the experiment, both our improved chaos genetic algorithm and the conventional genetic algorithm are utilized to solve the problem of mixed batch blending of Chinese herbal medicine. As the experimental results show, our improved chaos genetic algorithm is proved to be superior to the conventional genetic algorithm with respect to accuracy and efficiency.
PDF References
How to cite this article
Mingfeng Zhu, 2013. A Rapid Mixed Batch Blending Method Based on Chaos Genetic Algorithm. Information Technology Journal, 12: 3670-3673.
DOI: 10.3923/itj.2013.3670.3673
URL: https://scialert.net/abstract/?doi=itj.2013.3670.3673
DOI: 10.3923/itj.2013.3670.3673
URL: https://scialert.net/abstract/?doi=itj.2013.3670.3673
REFERENCES
- Huang, M. and Y.B. Wu, 2011. Remote sensing image classification based on Chaos Genetic algorithm. Sci. Surv. Mapping, 36: 5-8.
Direct Link - Liu, D. and Y. Cao, 2006. A chaotic genetic algorithm for fuzzy grid job scheduling. Proceedings of the International Conference on Computational Intelligence and Security, Volume 1, November 3-6, 2006, Guangzhou, China, pp: 320-323.
CrossRef - Liu, Y., M. Cao, Y. Chen, Y. Hu, Y. Wang and G. Luo, 2006. Study of blending method for the extracts of herbal plants. Chinese J. Chromatogr., 24: 117-121.
CrossRefDirect Link - Qu, H.B., D.L. Ou and Y.Y. Cheng, 2006. A new quality control method of Chinese medicinal plant extracts. Chinese Pharm. J., 41: 57-60.
Direct Link - Wang, H.J., F.Q. Deng and Z.M. Chen, 2011. Fuzzy LS-SVM classifier based on chaos genetic algorithm and its application. J. South China Univ. Technol. (Nat. Sci. Edn.), 39: 49-54.
Direct Link - Wang, L. and Z.X. Gong, 2011. K-anonymization based on genetic algorithm of variable length encoding. Comput. Eng., 37: 163-165.
Direct Link - Yang, H. and F. Wang, 2010. Application of multiobjective optimization to the blending of traditional Chinese medicine herbs. Proceedings of International Conference on Intelligent Computing and Integrated Systems, October 22-24, 2010, Guilin, pp: 468-471.
CrossRef - Yang, H.H., Y. Wang, H.Y. Zhang, Q.L. Liang, Y.M. Wang and G.A. Luo, 2007. Optimization method for blending traditional Chinese medicine herbs to ensuring the content stability of multiple indicative constituents. Chem. J. Chinese Univ., 28: 1863-1868.
Direct Link - Yang, M., Y. Zhou, J. Chen, M. Yu, X. Shi and X. Gu, 2009. Application of genetic algorithm in blending technology for extractions of cortex fraxini. China J. Chinese Materia Med., 34: 2594-2598.
PubMedDirect Link - Yan, T.S., 2009. Adaptive genetic algorithm simulating human reproduction mode and its application in multi-peak function optimization. Proceedings of the International Workshop on Intelligent Systems and Applications, May 23-24, 2009, Wuhan, pp: 1-4.
CrossRef - Zhao, X. and C.B. Xiu, 2011. New genetic algorithm improved and its applications. Proceedings of the International Conference on Electronics, Communications and Control, September 9-11, 2011, Ningbo, China, pp: 926-928.
CrossRef