工程优化设计网格的关键技术研究及其应用
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摘要
网格能够将互联网上分散在不同地理位置的资源组织起来,实现计算资源、存储资源、数据资源、信息资源等的全面共享,从而提供安全、可靠、廉价、高效的计算能力,协同解决复杂的大规模科学和工程计算问题。网格目前已经成为高性能计算领域新的研究热点和重要的发展方向。
现代工程优化设计的规模和复杂程度的大大提高,使得对高性能计算设备和环境的需求越来越强烈。网格能够整合网络上大量的闲置资源,提供超强的服务能力,这为进行大规模工程优化设计提供了可能。但是由于受到网格资源的分布性、异构性、动态性和自治性的限制,目前网格技术在工程优化设计领域还没有得到充分地应用。主要体现在:(1)缺少针对网格环境的优化算法和编程模型,难以通过网格技术高效地利用网络中的资源协同完成复杂的优化设计;(2)缺少对可协同优化作业的调度模型,限制了对基于网格的优化设计作业高效地调度;(3)缺少基于网格的优化设计平台及典型应用的研究,没有注重把大量闲置于网络的资源以“黑箱”服务的形式提供给实际的工程优化设计任务。
针对以上存在的问题,本文深入研究了基于网格的工程优化设计算法模型和针对优化作业的调度模型,搭建了工程优化设计网格平台EODG,并基于该平台开展了汽轮机基础优化和注塑成型优化等典型应用的研究。主要研究内容有:
1.发展了两种基于网格的黑箱优化算法模型,包括一种替代函数网格算法和一种演化设计网格算法。这两种算法以合理的分解优化设计任务和减少节点间数据传递量为基础,用计算池技术平衡网格节点的负载,既提高了算法精度又有效地加速了算法的收敛。替代函数网格算法给出一种与Kriging近似技术配套的序列优化迭代格式,可由少量抽样数据产生的不精确模型开始优化,并以较快的速度逼近最优解;演化设计网格算法采用多种群竞争和群体杂交策略,在传统遗传进化模型的基础上,利用定义最优解在各种群中出现的概率,引入信息熵目标,从而建立了熵基多种群演化设计模型,有效地加速了进化过程。
2.针对在广义网格环境中难以对高耦合任务所获得的服务质量进行评价的问题,建立了Multi-Qos评价模型。该模型在综合考虑了用户的多维Qos偏好和网格资源固有特征基础上,引入层次分析法,对网格任务所能获得服务质量进行多维的评价。
3.提出了基于资源监控和负载调整的优化作业网格调度算法(RMLABSA)。该算法通过监测信息对资源性能进行预测,利用多维Qos评价模型选取能够提供高服务质量的网格资源参与作业的协同执行;在此基础上,利用整数规划方法实现任务分配,并通过监控任务的执行获取资源服务能力的实测值,以此调整资源负载。该算法能够很好地平衡负载,具有较高的自适应性,适合对优化作业进行调度。
4.面向工程优化设计的共性需求,提出了四层的优化网格体系结构,搭建了工程优化设计网格计算平台(EODG)。层次化、构件化的设计使该系统具有透明性好、可靠性高、可伸缩性大和易维护性强等特点。基于该平台,研究了基于“黑箱”的优化程序和软件的网格化封装问题,实现了对汽轮机动力分析程序QJDU、注塑成型模拟程序Z-mold等软件的网格资源化封装,并把所提出的网格优化算法封装成为该平台上的优化设计构件。
5.开展基于网格的实际工程优化设计典型应用的研究。基于工程优化设计网格平台,利用所提出的网格计算方法,对汽轮机基础优化、注塑成型优化等典型应用进行了研究。算例的运行结果显示:(1)所提出的网格算法具有较高的精度和效率;(2)所搭建的网格平台适合应用于工程优化设计;(3)基于网格的大规模计算问题的效率更高。
本文工作得到国家自然科学基金项目(10590354)的资助。
The grid is to provide distributed computing infrastructure for advanced science and engineering problems by coordinated resource sharing and task solving in dynamic, multi-institutional virtual organizations. By means of grid the engineers can access computers, equipments, software, data base and other resources directly, but not files exchange only. At present, the grid techniques has developed very quickly and become a hot topic in high-performance computation field.
Optimization design plays very important role in the engineering applications, but it often involves huge computational efforts and requires powerful computing environment. The grid is a good choice for solving complex optimization design problems, because it integrates the massive idle resources to super-powerful environment. However, the distributed, dynamic and heterogeneous characteristics of grid environments put difficulties in engineering applications of the grid. Moreover, the serial and some parallel algorithms can not be run on the grid platform in a straightforward manner. Three main problems are encountered in the applications of grid to the modern engineering designs: (1) the physical and mathematical models for the engineering problems are even more complicated and difficult to optimize directly, so that some general analysis programs have to be used as black-box; (2) the highly heterogeneous and dynamic essence of the grid leads to the great difficulties for the grid resources distribution; (3) the researchers did not make efforts to develop the typical applications for engineering optimizations based on the grid.
In order to solve the complex optimization problems by using grid computing, two black-box optimization algorithms are proposed, which are an evolution grid algorithm and a surrogate function grid algorithm. For the surrogate function grid algorithm, a Kriging model is adopted to build the approximate relationship of objective and design variables with a Modified Rectangular Grid (MRG) sampling method, and the optimization iterations are based on the Kriging approximate relationship. The evaluation grid algorithm is based on the genetic algorithm, and some techniques are integrated in order to obtain good accuracy and efficiency in the grid environments, such as multi-population genetic strategy, entropy-based searching technique. In both of grid algorithms, independent meta-tasks are conducted in a collaborative way and computing pool technique is adopted to balance the computational loads.
Employing the Analytic Hierarchy Process (AHP) method, a multi-Qos model is proposed to evaluate the quality of grid service such as computing capacity, price, transfer performance, security and reliability of grid resources and user's benefits.
A Resource Monitoring and Load Adjusting Based Scheduling Algorithm(RMLABSA) model for independent tasks is presented in this work. The resource monitoring is aimed to find the real-time information before the task distribution and the real service ability during the computing. Integer programming method is adopted to carry out the distribution and adaptive adjusting technique is used to overcome the defaults of performance prediction for dynamic resources. The computational examples in this work show that the RMLABSA model balances the load well.
Four-layer Engineering Optimization Design Grid (EODG) platform is constructed for solving the complicated engineering optimization problems, which includes grid resources layer, grid middleware layer, optimization design & scheduling layer and user interface layer. Optimization algorithm and black-box programs are sealed as necessary components and the GT4 standard is used to obtain safe, reliable, cheaper using of the massive idle resources. The EODG is successfully applied to the turbine foundation optimization, injection gate location optimization, and injection processing optimization.
Some typical applications are implemented on the EODG employing the grid algorithms. The surrogate function grid algorithm is introduced to complete dynamic optimization design of the turbine engine foundation and processing optimization of injection of plastic production, and the evaluation grid algorithm is applied to injection design of gate location. The optimization results based on the grid show that the grid algorithms are very efficient and the grid platform is suitable for the engineering optimization designs.
This dissertation is financially supported by the National Natural Science Foundation (10272030).
现代工程优化设计的规模和复杂程度的大大提高,使得对高性能计算设备和环境的需求越来越强烈。网格能够整合网络上大量的闲置资源,提供超强的服务能力,这为进行大规模工程优化设计提供了可能。但是由于受到网格资源的分布性、异构性、动态性和自治性的限制,目前网格技术在工程优化设计领域还没有得到充分地应用。主要体现在:(1)缺少针对网格环境的优化算法和编程模型,难以通过网格技术高效地利用网络中的资源协同完成复杂的优化设计;(2)缺少对可协同优化作业的调度模型,限制了对基于网格的优化设计作业高效地调度;(3)缺少基于网格的优化设计平台及典型应用的研究,没有注重把大量闲置于网络的资源以“黑箱”服务的形式提供给实际的工程优化设计任务。
针对以上存在的问题,本文深入研究了基于网格的工程优化设计算法模型和针对优化作业的调度模型,搭建了工程优化设计网格平台EODG,并基于该平台开展了汽轮机基础优化和注塑成型优化等典型应用的研究。主要研究内容有:
1.发展了两种基于网格的黑箱优化算法模型,包括一种替代函数网格算法和一种演化设计网格算法。这两种算法以合理的分解优化设计任务和减少节点间数据传递量为基础,用计算池技术平衡网格节点的负载,既提高了算法精度又有效地加速了算法的收敛。替代函数网格算法给出一种与Kriging近似技术配套的序列优化迭代格式,可由少量抽样数据产生的不精确模型开始优化,并以较快的速度逼近最优解;演化设计网格算法采用多种群竞争和群体杂交策略,在传统遗传进化模型的基础上,利用定义最优解在各种群中出现的概率,引入信息熵目标,从而建立了熵基多种群演化设计模型,有效地加速了进化过程。
2.针对在广义网格环境中难以对高耦合任务所获得的服务质量进行评价的问题,建立了Multi-Qos评价模型。该模型在综合考虑了用户的多维Qos偏好和网格资源固有特征基础上,引入层次分析法,对网格任务所能获得服务质量进行多维的评价。
3.提出了基于资源监控和负载调整的优化作业网格调度算法(RMLABSA)。该算法通过监测信息对资源性能进行预测,利用多维Qos评价模型选取能够提供高服务质量的网格资源参与作业的协同执行;在此基础上,利用整数规划方法实现任务分配,并通过监控任务的执行获取资源服务能力的实测值,以此调整资源负载。该算法能够很好地平衡负载,具有较高的自适应性,适合对优化作业进行调度。
4.面向工程优化设计的共性需求,提出了四层的优化网格体系结构,搭建了工程优化设计网格计算平台(EODG)。层次化、构件化的设计使该系统具有透明性好、可靠性高、可伸缩性大和易维护性强等特点。基于该平台,研究了基于“黑箱”的优化程序和软件的网格化封装问题,实现了对汽轮机动力分析程序QJDU、注塑成型模拟程序Z-mold等软件的网格资源化封装,并把所提出的网格优化算法封装成为该平台上的优化设计构件。
5.开展基于网格的实际工程优化设计典型应用的研究。基于工程优化设计网格平台,利用所提出的网格计算方法,对汽轮机基础优化、注塑成型优化等典型应用进行了研究。算例的运行结果显示:(1)所提出的网格算法具有较高的精度和效率;(2)所搭建的网格平台适合应用于工程优化设计;(3)基于网格的大规模计算问题的效率更高。
本文工作得到国家自然科学基金项目(10590354)的资助。
The grid is to provide distributed computing infrastructure for advanced science and engineering problems by coordinated resource sharing and task solving in dynamic, multi-institutional virtual organizations. By means of grid the engineers can access computers, equipments, software, data base and other resources directly, but not files exchange only. At present, the grid techniques has developed very quickly and become a hot topic in high-performance computation field.
Optimization design plays very important role in the engineering applications, but it often involves huge computational efforts and requires powerful computing environment. The grid is a good choice for solving complex optimization design problems, because it integrates the massive idle resources to super-powerful environment. However, the distributed, dynamic and heterogeneous characteristics of grid environments put difficulties in engineering applications of the grid. Moreover, the serial and some parallel algorithms can not be run on the grid platform in a straightforward manner. Three main problems are encountered in the applications of grid to the modern engineering designs: (1) the physical and mathematical models for the engineering problems are even more complicated and difficult to optimize directly, so that some general analysis programs have to be used as black-box; (2) the highly heterogeneous and dynamic essence of the grid leads to the great difficulties for the grid resources distribution; (3) the researchers did not make efforts to develop the typical applications for engineering optimizations based on the grid.
In order to solve the complex optimization problems by using grid computing, two black-box optimization algorithms are proposed, which are an evolution grid algorithm and a surrogate function grid algorithm. For the surrogate function grid algorithm, a Kriging model is adopted to build the approximate relationship of objective and design variables with a Modified Rectangular Grid (MRG) sampling method, and the optimization iterations are based on the Kriging approximate relationship. The evaluation grid algorithm is based on the genetic algorithm, and some techniques are integrated in order to obtain good accuracy and efficiency in the grid environments, such as multi-population genetic strategy, entropy-based searching technique. In both of grid algorithms, independent meta-tasks are conducted in a collaborative way and computing pool technique is adopted to balance the computational loads.
Employing the Analytic Hierarchy Process (AHP) method, a multi-Qos model is proposed to evaluate the quality of grid service such as computing capacity, price, transfer performance, security and reliability of grid resources and user's benefits.
A Resource Monitoring and Load Adjusting Based Scheduling Algorithm(RMLABSA) model for independent tasks is presented in this work. The resource monitoring is aimed to find the real-time information before the task distribution and the real service ability during the computing. Integer programming method is adopted to carry out the distribution and adaptive adjusting technique is used to overcome the defaults of performance prediction for dynamic resources. The computational examples in this work show that the RMLABSA model balances the load well.
Four-layer Engineering Optimization Design Grid (EODG) platform is constructed for solving the complicated engineering optimization problems, which includes grid resources layer, grid middleware layer, optimization design & scheduling layer and user interface layer. Optimization algorithm and black-box programs are sealed as necessary components and the GT4 standard is used to obtain safe, reliable, cheaper using of the massive idle resources. The EODG is successfully applied to the turbine foundation optimization, injection gate location optimization, and injection processing optimization.
Some typical applications are implemented on the EODG employing the grid algorithms. The surrogate function grid algorithm is introduced to complete dynamic optimization design of the turbine engine foundation and processing optimization of injection of plastic production, and the evaluation grid algorithm is applied to injection design of gate location. The optimization results based on the grid show that the grid algorithms are very efficient and the grid platform is suitable for the engineering optimization designs.
This dissertation is financially supported by the National Natural Science Foundation (10272030).
引文
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