How to Read Many-Objective Solution Sets in Parallel Coordinates

TL;DR

This paper explores how to effectively interpret parallel coordinates plots for visualizing high-dimensional solution sets in many-objective optimization, providing guidelines for better understanding and comparison.

Contribution

It offers observations and insights into reading parallel coordinates plots, aiding in the analysis of solution quality, distribution, and objective relations in evolutionary many-objective optimization.

Findings

Guidelines for interpreting solution set quality and distribution.

Insights into understanding the shape and relations in high-dimensional data.

Enhanced methods for comparing solution sets visually.

Abstract

Rapid development of evolutionary algorithms in handling many-objective optimization problems requires viable methods of visualizing a high-dimensional solution set. Parallel coordinates which scale well to high-dimensional data are such a method, and have been frequently used in evolutionary many-objective optimization. However, the parallel coordinates plot is not as straightforward as the classic scatter plot to present the information contained in a solution set. In this paper, we make some observations of the parallel coordinates plot, in terms of comparing the quality of solution sets, understanding the shape and distribution of a solution set, and reflecting the relation between objectives. We hope that these observations could provide some guidelines as to the proper use of parallel coordinates in evolutionary many-objective optimization.