Multi-Objective Optimize The Extraction Medicine Effective Component Based On Multi-objective Genetic Algorithms

Multi-objective Genetic Algorithms (MOGA) is relative new in multi-objective optimization algorithm, many foreign scholars have conducted in-depth research and applied it to practical problems, but have relatively limited research in this area at home.especially in medicine areas, So for multi-objective genetic algorithm (MOGA) research is of great significance.Use matrix laboratory Matlab2009a software in the United States which math function is strong to draw function figure. SGALAB beta5of the Matlab2009a plug-in toolbox, which was provided by Chen Yi, achieves the genetic algorithm optimization; SPSS13.0software was used for statistical analysis.The main contents:Part one:An overview of the principles of the multi-objective genetic algorithm (MOGA).Part two:Effect evaluation and program testing of the multi-objective genetic algorithm (MOGA). Test the program with two goals, three goals and three standard test function,respectively.The result demonstrate that MOGA is search objective function value in the range of the independent variable, better approximation of the solution of the function, that MOGA the results are satisfactory, the program feasible.Part three:Exploratory study of multi-objective genetic algorithm (MOGA). Using multi-objective genetic algorithm (MOGA) for two goals, three goals, four goals drugs Optimization of extracting condition analysis.In the application of optimization of extraction technology of A canthopanax senticosus, the extraction yield and the content of total flavonoids are42.14%、 12.34%, the best extracting conditions are:crude drug of extracted with10times of76%alcohol for3times, each time for2.3h; and verification test results are good. MOGA provides a reasonable pareto ensuring the optimal multi-objective, which provides a reasonable method of choice for Acanthopanax senticosus.Three goals puerarin submicron emulsion preparation process, and the optimum process was optimized by using response surface method in the original conditions, compared to find the best combination of MOGA in the search results are better than the original experimental results. On four target optimization particle extraction process, the original optimum process conditions was optimized by using multi-index comprehensive evaluation method, and find the best combination of the original than MOGA compromise treatment was conducted on the search results, better to avoid a target and other objectives of poor situation, and it is within the scope of influence factors to search is not confined to limited take uniform experiment, at the same time provides researchers with a range of alternatives.According the MOGA procedure tests and examples to explore, you can think the MOGA you can provide a reference for solving practical problems.