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Towards automating the discovery of certain innovative design principles through a clustering-based optimization technique

机译：通过基于聚类的优化技术来实现对某些创新设计原理的自动化发现

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摘要

In this article, a methodology is proposed for automatically extracting innovative design principles which make a system or process (subject to conflicting objectives) optimal using its Pareto-optimal dataset. Such ‘higher knowledge’ would not only help designers to execute the system better, but also enable them to predict how changes in one variable would affect other variables if the system has to retain its optimal behaviour. This in turn would help solve other similar systems with different parameter settings easily without the need to perform a fresh optimization task. The proposed methodology uses a clustering-based optimization technique and is capable of discovering hidden functional relationships between the variables, objective and constraint functions and any other function that the designer wishes to include as a ‘basis function’. A number of engineering design problems are considered for which the mathematical structure of these explicit relationships exists and has been revealed by a previous study. A comparison with the multivariate adaptive regression splines (MARS) approach reveals the practicality of the proposed approach due to its ability to find meaningful design principles. The success of this procedure for automated innovization is highly encouraging and indicates its suitability for further development in tackling more complex design scenarios.
机译：在本文中，提出了一种方法，用于自动提取创新的设计原理，该原理使用其帕累托最优数据集使系统或过程（受目标冲突）最佳。这种“较高的知识”不仅可以帮助设计人员更好地执行系统，而且可以使他们预测如果系统必须保留其最佳性能，一个变量的变化将如何影响其他变量。反过来，这将有助于轻松解决其他具有不同参数设置的类似系统，而无需执行新的优化任务。所提出的方法使用基于聚类的优化技术，并且能够发现变量，目标函数和约束函数以及设计人员希望包含为“基本函数”的任何其他函数之间的隐藏函数关系。考虑到许多工程设计问题，这些显式关系的数学结构已经存在，并且已由先前的研究揭示。与多元自适应回归样条（MARS）方法的比较揭示了该方法的实用性，因为它能够找到有意义的设计原理。这种自动创新程序的成功令人鼓舞，并表明它适合进一步开发以应对更复杂的设计方案。

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  • 来源
    《Engineering Optimization》 |2011年第9期|p.911-941|共31页
  • 作者

    Sunith Bandaru;

  • 作者单位

    Department of Mechanical Engineering, Indian Institute of Technology Kanpur, UP, PIN 208016, India;

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