基于增强关联矩阵的多目标、多目标优化可视化

Amin Ibrahim, S. Rahnamayan, Miguel Vargas Martin, K. Deb
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引用次数: 0

摘要

多目标和多目标优化中近似解集的可视化是优化过程的一个重要组成部分。迄今为止,许多可视化技术的重点是说明解集的分布和收敛,而没有在决策变量和解集之间建立任何视觉联系。本文提出了一种简单的基于关联的可视化方案,称为增强相关矩阵图(Enhanced Correlation Matrix plot, ECM),它能够显示决策变量与目标值之间的关系。ECM图可以提供每个决策变量与目标函数之间的可视化关联信息,以及近似解集不同区域之间的客观关系。此外,它可以沿着每个目标提供可视化的解决方案分布。在三个广泛使用的2 - 8个目标基准问题和两个具有6和17个决策变量的现实问题上证明了该方法的有效性。实验结果表明,所提出的ECM图能够提供目标函数和目标-决策变量之间关系的基本信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced Correlation Matrix Based Visualization for Multi- and Many-objective optimization
Visualization of an approximate solution set in multi- and many-objective optimization is a crucial component of the optimization process. To date, the focus of many of the visualization techniques is illustration of the distribution and convergence of solutions set without making any visual connection between the decision variables and solutions set. This paper proposes simple correlation-based visualization scheme called Enhanced Correlation Matrix plot (ECM) capable of showing the relationship among decision variables and objective values. The ECM plot can provide visual correlation information between each decision variable and objective functions as well as objective-wise relationship for different regions of the approximated solution set. Moreover, it can provide visual distribution of solutions along each objective. The efficiency of the proposed method is demonstrated on three widely used two-to eight objective-benchmark problems and two real-world problems with 6 and 17 decision variables. The experimental results show that the proposed ECM plot can provide essential information pertaining to relationships among objective functions and objective-to-decision variables.
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