非支配排序遗传算法多目标优化金莲花水提工艺的研究

目的: 采用非支配排序遗传算法多目标优化金莲花水提工艺最优提取条件,并对其效果进行评价。 方法: 利用英国Glasgow大学软件工程师陈益提供的Matlab2009a外挂SGALAB工具箱beta5完成遗传算法寻优;SPSS13.0软件进行统计分析。 结果: 经过非支配排序遗传算法(NSGA)多目标优化后,金莲花水提的岀膏率、总黄酮含量平均水平能达到42.38%和6.84%,金莲花水提取工艺的最优提取条件为加水13倍量、浸泡0.84 h、煎煮3次、每次煎煮1.87 h,并且验证试验达到了较好的效果。 结论: 在保证多个目标都最优的前提下,NSGA搜索的帕累托非劣解比较理想,提供了可供研究者选择的Pareto非劣解方案,为试验设计最优条件选择提供了合理的方法。

Objective: TO optimize the optimal extracting conditions in micro-extraction technology of Floes Trollii by non-dominated sorting genetic algorithm(NSGA) in multi-objective optimization problem and evaluate the application and its effect. Methods: Used matrix laboratory Matlab2009a software in the British which math function was strong to draw function figure. SGALAB beta5 of the Matlab2009a plug-in toolbox , which was provided by Chen Yi in the United Kingdom University of Glasgow, achieves the genetic algorithm optimization; SPSS13.0 software was used for statistical analysis. Results: In the application of optimization of extraction technology of Floes Trollii, the extraction yield and the content of total flavonoids were 42.39% and 6.84%, the best extracting conditions were: 13 times the amount of water, soaked in 0.84 hours, boiling 3 times, each time was 1.87 hours,and verification test results were good. Conclusions: The variation of the pareto non-inferior solution of NSGA is little, in the sure of optimization the multi-objectives, which provides a reasonable method for selecting optimal conditions for experimental design.