【摘要】 针对多学科设计优化中传统静态代理模型全局收敛性差和计算效率低的问题,基于径向基函数提出了一类动态代理模型优化策略。利用最优拉丁超立方试验设计方法确定初始样本点,建立了径向基函数模型,综合应用信赖域方法和置信下限准则构造了动态样本空间;根据代理模型精度动态更新信赖域采样空间,通过代理模型预测值与真实模型响应值之间的误差,确定了置信下限中的自适应平衡常数,并采用遗传算法对置信下限准则进行了优化;结合数学测试算例和NASA减速器优化设计,对该方法的有效性进行了验证。研究结果表明:该方法是有效的,不仅能得到全局最优解,还能显著提高计算效率。更多还原

【Abstract】 Aiming at the problem of poor global convergence and low computational efficiency of traditional static metamodel in multidisciplinary design optimization, an optimization strategy for dynamic metamodel was proposed by combining trust region and adaptive lower confidence bound. In this strategy, the initial sampling points were selected by optimal Latin hypercube test design method and the radial basis function metamodel was constructed. The trust region method and the lower confidence limit criterion were used to construct the dynamic sample space. According to the accuracy of the agent model, the trust region sampling space was updated. The adaptive equilibrium constant in the lower confidence bound was determined by the error between the predictive value of agent model and response value of real model. The lower confidence bound was optimized by using genetic algorithm. The optimization strategy was verified by using a numerical test problem and the NASA speed reducer optimization design. The results indicate that the proposed method is proved to be effective,and can not only guarantee the optimal solution, but also significantly improve the computational efficiency.更多还原