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Model-based support for mutable, parametric, system-level design optimization.

机译：基于模型的可变，参数化，系统级设计优化支持。

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摘要

The design of dynamic computer controlled electromechanical systems with multiple and often conflicting optimization objectives presents formidable challenges. In many real world design problems, the relations between design variables and performance parameters are mutable, i.e., they vary with input tasks and system variables. Traditional methods for parametric design optimization in Operations Research and Artificial Intelligence, which assume that the relations between performance criteria and design variables are expressed in the form of invariant algebraic functions, do not apply to mutable optimization problems. Developing globally optimal design solutions requires a system level view of the design problem which accounts for dynamic interactions among the system's components in analyzing and optimizing its behavior for different system workloads.;This dissertation develops an intelligent support system to assist human designers in solving system-level, mutable, parametric design optimization problems. Given a description of the system's components and its configuration, we determine values for its design parameters that optimize specified objectives while meeting specified design constraints. We address the issue of system-level parametric design optimization by developing the framework for a compositional component-oriented system representation which facilitates reasoning about its behavior in a holistic manner. We develop model-based reasoning techniques to address two primary tasks that are key to mutable design optimization. The first task employs a structural model of the system to dynamically generate mutable optimization relations between the system's optimization objectives and its design variables. The second task employs sensitivity analysis techniques on the derived relations to efficiently navigate the design space in search of a good solution. We perform empirical analyses to demonstrate the effectiveness of our design optimization methodology using examples from the domain of reprographic machines.;This dissertation formally characterizes the mutable design optimization problem. The Environment Relationship net framework is adapted to build discrete event models of system behavior at suitable levels of abstraction that can be used for the purpose of system-level mutable parametric design optimization. Empirical analysis demonstrates that our design optimization methodology is both effective at finding good solutions and it does so quite efficiently by pruning large portions of the design space. This is achieved by the use model-based reasoning to guide the search process to promising regions of the design space without exhaustive search, the employment of cluster analysis to reduce the number of jobs considered during the optimization process, and the randomization of the algorithm that searches for optimal solutions to eliminate local optima.
机译：具有多个且经常相互冲突的优化目标的动态计算机控制的机电系统的设计提出了巨大的挑战。在许多现实世界的设计问题中，设计变量和性能参数之间的关系是可变的，即，它们随输入任务和系统变量而变化。运筹学和人工智能中用于参数设计优化的传统方法假定性能标准和设计变量之间的关系以不变代数函数的形式表示，但不适用于可变的优化问题。开发全球最佳的设计解决方案需要在系统层面上了解设计问题，从而在分析和优化针对不同系统工作负载的行为时，考虑系统组件之间的动态交互作用。本论文开发了一种智能支持系统，以帮助人类设计师解决以下问题：级别，可变，参数化设计优化问题。在给出了系统组件及其配置的描述后，我们确定其设计参数的值，这些参数可以在满足指定设计约束的同时优化指定目标。我们通过开发面向组成组件的系统表示的框架来解决系统级参数设计优化的问题，该框架有助于以一种整体的方式对其行为进行推理。我们开发了基于模型的推理技术，以解决可变设计优化关键的两个主要任务。第一项任务采用系统的结构模型来动态生成系统优化目标与其设计变量之间的可变优化关系。第二项任务是对派生的关系采用敏感性分析技术，以有效地导航设计空间，以寻找良好的解决方案。我们进行了实证分析，以复制机器领域的实例为例，证明了我们的设计优化方法的有效性。本论文正式描述了可变设计优化问题。环境关系网框架适用于在适当的抽象级别构建系统行为的离散事件模型，该模型可用于系统级可变参数设计优化的目的。经验分析表明，我们的设计优化方法既可以有效地找到良好的解决方案，又可以通过修剪大部分设计空间来高效地做到这一点。这是通过基于模型的推理将搜索过程引导到设计空间的有希望的区域而无穷举搜索，采用聚类分析以减少优化过程中考虑的工作数量以及算法的随机化来实现的。搜索最佳解决方案以消除局部最优。

著录项

  • 作者

    Kapadia, Ravi Pankaj.;

  • 作者单位

    Vanderbilt University.;

  • 授予单位 Vanderbilt University.;
  • 学科 Artificial Intelligence.;Computer Science.
  • 学位 Ph.D.
  • 年度 1999
  • 页码 149 p.
  • 总页数 149
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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