基于数据采掘工具的多准则决策预处理
Multiple Criteria Decision-Making Preprocessing Using Data Mining Tools
摘要：
现实生活中的工程优化问题需要多目标优化（MOO）工具。这些问题是高度非线性的。随着多准则决策过程的不断扩展，不同学科的多目标优化问题都可以在此基础上进行分类，因此多准则决策方法在不同的学科和应用领域得到了广泛的应用。同时，过程中涉及的组件、变量、参数、约束和目标的数量越来越多，使得过程变得非常复杂。虽然新一代的MOO工具使优化过程更加自动化，但是初始化过程和设置仿真工具的初始值以及识别有效的输入变量和目标以达到更小的设计空间仍然是复杂的。在这种情况下，在MCDM过程中添加预处理步骤可能会在根据输入变量对系统优化目标的影响来组织输入变量方面产生巨大的差异。本文的目的是介绍分类任务的数据采掘作为一个有效的选择，确定最有效的变量的MCDM系统。为了验证所提方法的有效性，给出了一个三维机翼设计的算例。
英文摘要：
Real-life engineering optimization problems need Multiobjective Optimization (MOO) tools. These problems are highly nonlinear. As the process of Multiple Criteria Decision-Making (MCDM) is much expanded most MOO problems in different disciplines can be classified on the basis of it. Thus MCDM methods have gained wide popularity in different sciences and applications. Meanwhile the increasing number of involved components, variables, parameters, constraints and objectives in the process, has made the process very complicated. However the new generation of MOO tools has made the optimization process more automated, but still initializing the process and setting the initial value of simulation tools and also identifying the effective input variables and objectives in order to reach the smaller design space are still complicated. In this situation adding a preprocessing step into the MCDM procedure could make a huge difference in terms of organizing the input variables according to their effects on the optimization objectives of the system. The aim of this paper is to introduce the classification task of data mining as an effective option for identifying the most effective variables of the MCDM systems. To evaluate the effectiveness of the proposed method an example has been given for 3D wing design.
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