Adjusting parallel coordinates for investigating multi-objective search

Liangli Zhen*, Miqing Li, Ran Cheng, Dezhong Peng, Xin Yao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)
154 Downloads (Pure)


Visualizing a high-dimensional solution set over the evolution process is a viable way to investigate the search behavior of evolutionary multi-objective optimization. The parallel coordinates plot which scales well to the data dimensionality is frequently used to observe solution sets in multi-objective optimization. However, the solution sets in parallel coordinates are typically presented by the natural order of the optimized objectives, with rare information of the relation between these objectives and also the Pareto dominance relation between solutions. In this paper, we attempt to adjust parallel coordinates to incorporate this information. Systematic experiments have shown the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationSimulated Evolution and Learning
Subtitle of host publication11th International Conference, SEAL 2017, Proceedings
EditorsXiaodong Li, Mengjie Zhang, Qingfu Zhang, Martin Middendorf, Kay Chen Tan, Ying Tan, Yaochu Jin, Yuhui Shi, Ke Tang
Number of pages12
ISBN (Electronic)9783319687599
ISBN (Print)9783319687582
Publication statusPublished - 14 Oct 2017
Event11th International Conference on Simulated Evolution and Learning, SEAL 2017 - Shenzhen, China
Duration: 10 Nov 201713 Nov 2017

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th International Conference on Simulated Evolution and Learning, SEAL 2017

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Adjusting parallel coordinates for investigating multi-objective search'. Together they form a unique fingerprint.

Cite this