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Algorithme intelligent d'optimisation d'un design structurel de grande envergure.

机译：用于优化大型结构设计的智能算法。

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摘要

The implementation of an automated decision support system in the field of design and structural optimisation can give a significant advantage to any industry working on mechanical designs. Indeed, by providing solution ideas to a designer or by upgrading existing design solutions while the designer is not at work, the system may reduce the project cycle time, or allow more time to produce a better design.This approach was developed in order to reduce the operating cost of the process. However, as the system implementation cost is quite expensive, the approach is better suited for large scale design problem, and particularly for design problems that the designer plans to solve for many different specification sets.First, the CBR process uses a databank filled with every known solution to similar design problems. Then, the closest solutions to the current problem in term of specifications are selected. After this, during the adaptation phase, an artificial neural network (ANN) interpolates amongst known solutions to produce an additional solution to the current problem using the current specifications as inputs.Each solution produced and selected by the CBR is then used to initialize the population of an island of the genetic algorithm. The algorithm will optimise the solution further during the refinement phase.This thesis presents a new approach to automate a design process based on Case-Based Reasoning (CBR), in combination with a new genetic algorithm named Genetic Algorithm with Territorial core Evolution (GATE).Using progressive refinement, the algorithm starts using only the most important variables for the problem. Then, as the optimisation progress, the remaining variables are gradually introduced, layer by layer.The genetic algorithm that is used is a new algorithm specifically created during this thesis to solve optimisation problems from the field of mechanical device structural design. The algorithm is named GATE, and is essentially a real number genetic algorithm that prevents new individuals to be born too close to previously evaluated solutions. The restricted area becomes smaller or larger during the optimisation to allow global or local search when necessary.Also, a new search operator named Substitution Operator is incorporated in GATE. This operator allows an ANN surrogate model to guide the algorithm toward the most promising areas of the design space.The suggested CBR approach and GATE were tested on several simple test problems, as well as on the industrial problem of designing a gas turbine engine rotor's disc.These results are compared to other results obtained for the same problems by many other popular optimisation algorithms, such as (depending of the problem) gradient algorithms, binary genetic algorithm, real number genetic algorithm, genetic algorithm using multiple parents crossovers, differential evolution genetic algorithm, Hookes & Jeeves generalized pattern search method and POINTER from the software I-SIGHT 3.5.Results show that GATE is quite competitive, giving the best results for 5 of the 6 constrained optimisation problem. GATE also provided the best results of all on problem produced by a Maximum Set Gaussian landscape generator. Finally, GATE provided a disc 4.3% lighter than the best other tested algorithm (POINTER) for the gas turbine engine rotor's disc problem.One drawback of GATE is a lesser efficiency for highly multimodal unconstrained problems, for which he gave quite poor results with respect to its implementation cost.To conclude, according to the preliminary results obtained during this thesis, the suggested CBR process, combined with GATE, seems to be a very good candidate to automate and accelerate the structural design of mechanical devices, potentially reducing significantly the cost of industrial preliminary design processes.
机译：在设计和结构优化领域中实现自动化决策支持系统可以为从事机械设计的任何行业带来显着优势。实际上，通过向设计师提供解决方案构想或在设计师不在工作时升级现有的设计解决方案，系统可以减少项目周期时间，或留出更多时间来产生更好的设计。该过程的运营成本。但是，由于系统实施成本非常昂贵，因此该方法更适合于大规模设计问题，尤其是设计人员计划解决的许多不同规范集的设计问题。首先，CBR过程使用一个数据库，每个数据库都充满了已知解决类似设计问题的方法。然后，在规格方面选择与当前问题最接近的解决方案。之后，在适应阶段，人工神经网络（ANN）在已知解决方案中进行插值，以使用当前规范作为输入来解决当前问题的其他解决方案，然后使用CBR生成和选择的每个解决方案来初始化总体遗传算法的一个岛。该算法将在细化阶段进一步优化解决方案。本文提出了一种基于案例推理（CBR）的新方法，该方法结合了一种新的遗传算法，具有地域核心演化的遗传算法（GATE），可以使设计过程自动化。使用渐进式细化，该算法仅开始使用最重要的变量解决问题。然后，随着优化的进行，逐步将剩余的变量逐层引入。遗传算法是本论文专门为解决机械设备结构设计领域的优化问题而专门创建的一种新算法。该算法称为GATE，本质上是一种实数遗传算法，可以防止新个体出生时过于接近先前评估的解决方案。在优化过程中，限制区域会变小或变大，以便在必要时进行全局或局部搜索。此外，GATE中还合并了一个名为Substitution Operator的新搜索运算符。该运算符允许ANN替代模型将算法引导到设计空间中最有希望的领域。建议的CBR方法和GATE在几个简单的测试问题上以及在设计燃气涡轮发动机转子盘的工业问题上进行了测试将这些结果与通过许多其他流行的优化算法针对相同问题获得的其他结果进行比较，例如（取决于问题）梯度算法，二元遗传算法，实数遗传算法，使用多个双亲交叉的遗传算法，差分进化遗传算法，Hookes＆Jeeves通用模式搜索方法和来自软件I-SIGHT 3.5的POINTER。结果表明，GATE具有相当的竞争力，在6个约束优化问题中有5个给出了最佳结果。 GATE还提供了最大集高斯景观生成器产生的所有问题的最佳结果。最后，GATE提供的光盘比用于燃气轮机转子的光盘问题的最佳其他测试算法（POINTER）轻4.3％.GATE的一个缺点是对高度多模态无约束问题的效率较低，因此他给出的结果相当差总而言之，根据本文获得的初步结果，建议的CBR工艺与GATE结合使用，似乎是自动化和加速机械设备结构设计的非常不错的选择，有可能显着降低成本工业初步设计过程。

著录项

  • 作者

    Dominique, Stephane.;

  • 作者单位

    Ecole Polytechnique, Montreal (Canada).;

  • 授予单位 Ecole Polytechnique, Montreal (Canada).;
  • 学科 Engineering Aerospace.Artificial Intelligence.Engineering Mechanical.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 358 p.
  • 总页数 358
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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