The Application of Logratio Transform and PSO-BP Neural Network in the Optimization of Multi-objective Mixture Design Drug Prescription Ratio
10.11783/j.issn.1002-3674.2025.01.008
- VernacularTitle:Logratio变换与PSO-BP神经网络在多目标混料设计药物处方配比优化中的应用
- Author:
Yiting LI
1
;
Yuchao QIAO
1
;
Xuchun WANG
1
Author Information
1. 山西医科大学公共卫生学院卫生统计学教研室(030001)
- Publication Type:Journal Article
- Keywords:
Mixture uniform design;
Logratio transformation;
PSO-BP;
Nondominated sorting genetic algorithm-Ⅱ
- From:
Chinese Journal of Health Statistics
2025;42(1):44-49
- CountryChina
- Language:Chinese
-
Abstract:
Objective In order to provide a scientific and reasonable method for the optimization of drug mix design,the application of PSO-BP neural network modeling after Logratio transformation and nondominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)optimization in the optimization of drug prescription ratio of multi-objective mix design was explored.Methods Based on the analysis of experimental data in literature,after Logratio transformation of experimental data of compound glycyrrhiza microemulsion,PSO-BP neural network model was constructed by taking particle size and skin retention of active components as evaluation indexes,and then NSGA-Ⅱ was adopted for multi-objective optimization of the network.Finally,the optimization scheme in this paper was compared with that in the original paper.Results The fitting effect of PSO-BP neural network using particle size and active component skin retention as output is R2=0.97298 and R2=0.96334,respectively,indicating that the fitting effect of PSO-BP is better,and the fitting effect is improved compared with the Scheffe polynomial model used in the original paper.In this paper,PSO-BP was used to construct the model,and NSGA-Ⅱ scheme 3、4、6、7、10、11 etc.were superior to the original scheme.Compared with the original scheme,the microemulsion particle size was reduced by 3.02nm and the skin retention of the active ingredient was increased by 18.31 μg.Conclusion In theory,it is feasible and reasonable to use Logratio transformation and PSO-BP neural network in the model construction of mixed data and NSGA-Ⅱalgorithm to obtain the optimal ratio of drug prescription.
