【作者】 向国齐；

【导师】 黄大贵；

【作者基本信息】 电子科技大学 ， 机械电子工程， 2010， 博士

【摘要】 制造业是衡量一个国家的重要支柱产业,它的强弱将影响到综合国力的兴衰。随着各学科理论和计算机仿真技术的不断发展,现代产品的研发通常采用基于仿真的设计优化,但很多情况下,产品涉及多个不同学科领域,而且各学科的仿真模型可能非常复杂,要获得理想的优化结果需要各学科分析模型之间多次迭代才能完成,计算时间的大量耗费往往令人无法接受。同时,制造业的主要竞争目标是缩短产品设计和制造周期,最终达到降低产品开发成本目的。因此,计算复杂性是复杂产品研发中面临的一个重要问题。代理模型技术是解决以上问题的有效途径,但目前常用的代理模型对于多变量和强非线性的优化问题,逼近的效果不是很理想。为此,本文将良好性能的机器学习模型支持向量回归机引入工程优化问题,采用支持向量回归机代理模型对复杂产品设计优化进行了深入研究,开展了以下几个方面的研究工作,并取得了相关的研究成果。1)回顾了常用代理模型和试验设计的基本理论,指出了它们各自的优缺点与适用场合;阐述了统计学习理论,提出了支持向量回归机代理模型构建方法及详细步骤;并以2个实例验证了模型的有效性。2)提出了基于SVR-GA的优化方法、基于SVR-PSO的优化方法和基于SVR-NSGAII的优化方法;详细阐述了这些方法的算法流程;以工程多目标优化问题实例,验证了它们的有效性和可行性。较好地解决了小样本、高维数、非线性、泛化性能、局部极小点等复杂工程优化问题。3)研究了不确定因素对产品质量特性的影响机理,提出了多目标稳健优化的数学模型;将支持向量回归机代理模型引入稳健优化,提出了基于支持向量回归机代理模型的稳健优化方法,并详细阐述该算法流程;以典型的两杆结构优化问题对所提出方法进行验证,比较研究了不同代理模型在逼近具有不确定因素的优化模型时的性能,验证了该方法的有效性。4)介绍了五种代表性的多学科设计优化方法,并分析了各自的优缺点。指出了目前多学科协同优化方法存在的问题,提出了基于支持向量回归机代理模型的多学科协同优化方法,建立了该算法的数学模型,并详细阐述了算法流程。以典型的耦合优化问题算例对SVR-CO方法进行验证,比较研究了SVR-CO方法、标准CO与MDF方法的优化效果,验证了该方法的有效性。更多还原

【Abstract】 Manufacturing is a measure of a country’s pillar industry, and influence national comprehensive strength. With the development of disciplinary theory and computer simulation technology, complex mechanical product typically requires extensive use of simulation-based design and analysis tools, Despite the steady and continuing growth of computing power and speed, the computational cost of complex high-fidelity engineering analyses and simulations maintains pace. The high computational expense limits, or often prohibits, the use of such codes in engineering design and multidisciplinary design optimization (MDO). Meanwhile, the manufacturing industry competitively aims at shortening the product development and manufacturing cycles and reducing product development costs. Therefore, the conflicts between computational Accuracy and efficiency are is an important issue for engineering design of complex products.Metamodeling techniques are widely used in engineering design to address these concerns. The basic approach is to construct approximations of the analysis codes that are more efficient to run, and yield insight into the functional relationship between design variables and response. In this work, we investigate support vector regression (SVR) as a metamodel for approximating complex engineering analyses, and explores the basic theory and the key implementation technologies on metamodel based on support vector regression for engineering optimization problems. The dissertation carried out researches on the following topics and obtained the corresponding results.1) By comparing the advantages and disadvanteges of existing kinds of popolar metamodel methodology, SVR metamodel method was proposed. By using testing functions and engineering example to make comparative research on the precision of approximate models, results show SVR metamodel method is high efficiency and precision.2) Aiming at the optimization design problem with implicit objective performance functions, a design optimization method based on SVR metamodel and genetic algorithm (GA) is proposed, a framework based on the SVR and particle swarm optimization (PSO) for structure optimization design, and a multiobjective design optimization method based on SVR metamodel and improved Non-dominated Sorting Genetic Algorithm (NSGA-II) is proposed. The structure optimization of a microwave power divider is adopted as an example to illustrate the effectiveness of these design methods.3) Aiming at the robust optimization with uncertainty design problem of computationally intensive simulation models, a reduced approximation model technique based on SVR is introduced in order to improve the accuracy of metamodel. A framework based on SVR and GA is presented for robust optimization problems. The performances of SVR were compared with other existing metamodels under uncertainty. The applicability of the method is demonstrated using a two-bar structure system study, the results showed that the prediction accuracy of SVR model was higher than those of others metamodels, and the proposed optimization methodology is found to be accurate and efficient for robust optimization.4) Exiting MDO methods are reviewed, and the advantages and disadvantages of these methods are discussed and analyzed. Collaborative optimization (CO) is systematically investigated. In order to deal with complicated MDO problem, a novel MDO CO method based on SVR metamodel (SVR-CO) is proposed. By using typical coupled optimization example to make comparative research of three methods including SVR-CO, CO and Multidisciplinary Feasibility Method (MDF), SVR-CO is proven to be efficient and effective.更多还原