A radial visualization method based on knee point information for many-objective optimization

IF 7.2 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Applied Soft Computing Pub Date : 2025-04-05 DOI:10.1016/j.asoc.2025.113064

Gui Li , Renbin Xiao

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引用次数: 0

Abstract

Numerous high-dimensional solutions for many-objective optimization problems (MaOPs) usually impose a high cognitive burden on decision makers (DMs). Pareto front (PF) of MaOPs can express the problem characteristics, and then provide prior knowledge for solving the MaOPs. However, the existing high-dimensional visualization methods usually do not establish the relationship between PF information and decision making. Therefore, a novel radial visualization (RadViz) method called KRadViz that incorporates knee point information is proposed to visualize the information of PF shape and aid decision making. The relationship between the optimized performance information and PF shape is established, and the PF shape identification method is constructed. KRadViz is constructed by combining the optimization performance and PF shape. Three preferred solution selection methods are proposed to quickly screen out a few preferred solutions in different scenarios. The proposed KRadViz is compared with three high-dimensional visualization methods. The experimental results show that KRadViz can effectively display the high-dimensional PF shape, and give the optimization performance information of different solutions. The selection preferences of the three methods are also analyzed, and the effectiveness of the assisted decision process is verified. For the DTLZ2 and real-world MaOPs, the individual hypervolume (HV) contribution of preferred solutions increased by 9.98 % and 10.95 %, respectively.

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基于膝点信息的径向可视化多目标优化方法

多目标优化问题的大量高维解通常给决策者带来很高的认知负担。MaOPs的Pareto front （PF）可以表达问题的特征，从而为MaOPs的求解提供先验知识。然而，现有的高维可视化方法通常没有建立PF信息与决策之间的关系。为此，提出了一种结合膝关节点信息的径向可视化方法（RadViz），以实现膝关节形状信息的可视化，为决策提供依据。建立了优化后的性能信息与PF形状之间的关系，构建了PF形状识别方法。KRadViz是将优化性能与PF形状相结合而构建的。提出了三种优选方案选择方法，以便在不同场景下快速筛选出一些优选方案。并与三种高维可视化方法进行了比较。实验结果表明，KRadViz能够有效地显示高维PF形状，并给出不同解的优化性能信息。分析了三种方法的选择偏好，验证了辅助决策过程的有效性。对于DTLZ2和实际MaOPs，首选解决方案的单个超容量（HV）贡献分别增加了9.98 %和10.95 %。

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来源期刊

Applied Soft Computing 工程技术-计算机：跨学科应用

CiteScore

15.80

自引率

6.90%

发文量

874

审稿时长

10.9 months

期刊介绍： Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.