Abstract
Rapid development of evolutionary algor ithms in handling many-objective optimization problems requires viable methods of visualizing a high-dimensional solution set. The parallel coordinates plot which scales well to high-dimensional data is such a method, and has 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 the parallel coordinates plot in evolutionary manyobjective optimization. © 2005-2012 IEEE.
| Original language | English |
|---|---|
| Article number | 8065139 |
| Pages (from-to) | 88-100 |
| Number of pages | 13 |
| Journal | IEEE Computational Intelligence Magazine |
| Volume | 12 |
| Issue number | 4 |
| Early online date | 11 Oct 2017 |
| DOIs | |
| Publication status | Published - Nov 2017 |
| Externally published | Yes |
Funding
The authors would like to thank the Associate Editor and the three reviewers for their thoughtful suggestions and constructive comments. This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of U.K. under Grants EP/K001523/1 and EP/J017515/1, and National Natural Science Foundation of China (NSFC) under Grants 61329302 and 61403326. X. Yao was also supported by a Royal Society Wolfson Research Merit Award.