The application of Mendelian multi-objective simple genetic algorithms in the optimization of extraction conditions of medicine

Shuai Dai, Xianfeng Shi, Ting Wang, Ruimei Feng, Yi Chen, Lixia Qiu

Research output: Contribution to journalArticlepeer-review


Objective Study the application of optimization analysis of Mendelian Multi-objective Simple Genetic Algorithms in drug extraction. Methods Using microw ave extraction Radix acanthopanacis senticosi data in uniform design establish three-objective function. Using SGALAB beta5008 of the Matlab 2009 a plug-in tool-box,w hich w as w ritten by ChenYi in the United Kingdom University of Glasgow,achieves the genetic algorithm optimization. Applying simple genetic algorithm and MMOSGA explore the optimal extracting conditions. Compare their optimal extracting conditions. Results Single-objective genetic algorithm optimization can obtain the optimal extraction conditions of each objective. MMOSGA for three-objective optimization,w hich can achieve 71% of the maximum value of single objective function in the main goals,thus w e can get optimal extraction conditions,the effect of w hich is best. Conclusion MMOSGA provides reasonable pareto optimal solutions. It is a reasonable method for selecting optimal conditions for Uniform Experimental Design. This method can be extended to the selection of the optimal conditions in the orthogonal experimental design and factorial design.
Original languageChinese
Pages (from-to)615-619
Number of pages5
JournalChinese Journal of Health Statistics
Issue number4
Publication statusPublished - 2014


  • genetic algorithms
  • Pareto non-inferior solution
  • drug extractions

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