不依赖于统计模型的手性毛细管电泳分离多指标同时优化

考虑到手性毛细管电泳分离机理的复杂性，提出了不依赖于统计模型的二进制编码遗传算法多目标优化策略。根据实验参数的多少及其重要性，采用均匀设计安排完成初步实验。并用Derringer功效函数作为多指标同时优化的评价指标，以取得较高的分离度和较快分离速度；通过对实验参数给予编码，转化为二进制字符串，根据得到的总功效函数值大小进行遗传算法操作，产生新的一组实验条件。重复上述过程直到得到最佳分离条件为止。此方法成功地应用于华法令对映体的毛细管电泳分离条件优化。

Multicriteria-decision-making Simultaneous Optimization Independent of Statistical Models in Chiral Capillary Electrophoresis Separation

Considering the complexity of chiral capillary electrophoresis separation mechanism, a multicriteria-decision-making optimization approach based on binary-coded genetic algorithm, which was independent of the statistic model used, was developed. According to the experiment parameters and their importances, the initial experiments were arranged using a uniform design. In order to achieve the simultaneous optimization of the two major electrophoretic performance goals for efficient and fast separations, the Derringer desirability functions were chosen as the optimization criteriaon. The selected parameters were encoded into binary bit strings, and then genetic algorithm calculations were performed to generate a new set of experimental ~conditions by the values of total desirability functions. This cycle process was repeated until a best separation criterion was reached. The method proposed was successfully applied to the enantioseparation of warfarin by chiral capillary electrophoresis.