混合元模型自适应空间探索优化方法及应用

Hybrid metamodel-based adaptive space exploration optimization method and application

为了提高翼身融合水下滑翔机等复杂工程系统的设计质量与优化效率,本文提出一种混合元模型自适应空间探索优化方法。通过增强随机进化算法选取初始样本点,构造3种经典单一元模型:多项式响应面模型、径向基函数模型和克里金模型。使用基于预测均方根误差权重因子优化方法计算获得各元模型权系数,通过加权叠加构建混合元模型。采用自适应空间探索方法根据已知信息确定有效的设计子空间,并在其内部选取新增样本点,逐步提高混合元模型在感兴趣区域的预测精度,直至优化收敛。将提出的方法应用于标准优化函数和翼身融合水下滑翔机外形设计优化实例,试验对比结果验证了该方法在优化效率和全局收敛性方面的优势。

To improve the design quality and optimization efficiency for complicated engineering systems,such as blended wing body underwater gliders,a hybrid metamodel-based adaptive space exploration optimization method is proposed in this paper.Three classical metamodels,including polynomial response surfaces,radial basis functions,and kriging,are constructed using the initial sample points generated by the enhanced stochastic evolutionary method.The hybrid metamodels can be constructed using the weighted superposition of the weight factors of each metamodel based on the predicted root mean square error.The adaptive space exploration method is used to identify the promising design subspace according to the known information.Then,new sample points are selected in this promising design subspace.Thus,the prediction accuracy in the region of interest can be gradually enhanced until the optimization convergence.Based on the testing results using eight benchmark optimization functions and blended wing body underwater glider shape design optimization problem,the newly proposed optimization method shows improved capability in terms of the optimization efficiency and global optimum identification.