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Development of non-dominated sorting genetic quantum algorithm for structural and tool shape optimization.

机译：开发用于结构和工具形状优化的非支配排序遗传量子算法。

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摘要

Structural optimization is important for product design in order to improve the performance and decrease the production time as well as total cost of a product. In the past, engineers used costly and time consuming trial-and-error methods to find the optimum for their structural design problems. Today, with steadily increasing computational power, computers are increasingly used to design, manufacture, and evaluate the performance of a product without having to physically building it to reduce the cost of development. The finite element analysis (FEA) used in such developmental process are expensive and hence, often hinders the direct use of conventional optimization techniques such as the mathematical programming and most heuristic approaches. When there are multiple objectives and constraints, the difficulty compounds. Efficient optimization of structural design problems with computationally intensive FEA processes has been studied intensely by many researchers for the past decade. Only recently, researchers have started to focus on optimization of multiple objective problems using these expensive FEA. This thesis aims to develop a multi-objective optimization algorithm which can handle multiple design variable, objectives, and constraints of expensive FEA models. A new algorithm, called non-dominated sorting genetic quantum algorithm (NSGQA), is developed by integrating a newly developed evolutionary algorithm, genetic quantum algorithm (GQA), and a multi-objective sorting mechanism. The performance of NSGQA is intensively evaluated using inexpensive optimization problems and compared with state-of-the-art meta-heuristics multi-objective optimization algorithms. Subsequently, NSGQA is applied to a real structural optimization problem with 47 trusses and many constraints. From the tests and application to structural optimization, NSGQA demonstrates promising performance. The NSGQA is integrated with a FEA-based process model for composite processing and is applied to optimize tool shape to reduce the process-induced warpage. This preliminary benchmarking was done to pave the way for further research on multi-objective tool shape optimization for composite processing.
机译：结构优化对于产品设计很重要，以提高性能并减少生产时间以及产品的总成本。过去，工程师使用昂贵且耗时的反复试验方法来找到针对其结构设计问题的最佳方法。如今，随着计算能力的不断提高，计算机已越来越多地用于设计，制造和评估产品的性能，而无需进行物理构建以降低开发成本。在这种开发过程中使用的有限元分析（FEA）成本很高，因此，通常会阻碍直接使用常规优化技术（例如数学编程和大多数启发式方法）。当有多个目标和约束时，难度就会增加。在过去的十年中，许多研究人员对使用计算密集型FEA流程进行的结构设计问题的有效优化进行了深入研究。直到最近，研究人员才开始专注于使用这些昂贵的FEA优化多目标问题。本文旨在开发一种多目标优化算法，该算法可以处理昂贵的FEA模型的多个设计变量，目标和约束。通过集成新开发的进化算法，遗传量子算法（GQA）和多目标排序机制，开发了一种称为非主导排序遗传量子算法（NSGQA）的新算法。使用便宜的优化问题对NSGQA的性能进行了深入评估，并与最新的元启发式多目标优化算法进行了比较。随后，将NSGQA应用于具有47个桁架和许多约束的实际结构优化问题。从测试和应用到结构优化，NSGQA展现出令人鼓舞的性能。 NSGQA与基于FEA的复合材料加工过程模型集成在一起，并用于优化工具形状以减少过程引起的翘曲。进行此初步基准测试是为进一步研究复合加工的多目标刀具形状铺平道路。

著录项

  • 作者

    Khorsand, Amirreza.;

  • 作者单位

    University of Manitoba (Canada).;

  • 授予单位 University of Manitoba (Canada).;
  • 学科 Engineering Mechanical.
  • 学位 M.Sc.
  • 年度 2009
  • 页码 106 p.
  • 总页数 106
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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