针对非线性优化问题约束条件中待定参数的时间序列数据,首先使用GM（1,1)方法进行建模预测得到参数的预测值,进而将参数预测值代入原问题中提出一个确定型的非线性优化问题。对该确定型问题设计了一个多子种群并行进化的遗传算法进行求解,在分析所提算法的收敛性的基础上,给出了初步的数值算例。数值算例实验结果表明：该算法能够较为精确地获得预测型非线性优化问题的（近似)全局最优解。%According to the historical data of the time series of the constraints parameters in the nonlinear optimization prob-lem, the values of the constraints parameters are modeled and predicted using GM(1,1) method. Substituting the predicted values of the parameters into the grey nonlinear optimization problem, we obtain a determinate nonlinear optimization problem. Then, we design a multi-subgroup genetic algorithm genetic algorithm to solve the resulting problem. Moreover, we analyze the convergence of our method and develop preliminary the numerical experiment which shows that the ( approximated) global optimal solution of the predicted nonlinear optimization problem can be found using the proposed method.
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