Visualization on pareto solutions in multi-objective optimization

Shin Ichi Ito, Yasue Mitsukura, Takafumi Saito, Katsuya Sato, Shoichiro Fujisawa

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

Abstract

This paper introduces a method for visualizing the relationship between optimized elements and their evaluation values in multi-objective optimization using the pseudo coloring method in information visualization techniques. Because multi-objective optimal problem has a lot of optimal solutions (Pareto solution), it is not easy to choose a single optimal solution. There is a tendency that it is confirmed not only the evaluation values but also the optimized elements are necessary when designers specify an optimal solution. Then, we focus on a real-coded genetic algorithm that is one of the multi-objective optimization techniques. The proposed method visualizes the relationship between the gene values, which indicate the optimized elements, and objective values, which denote the evaluation values, of all individuals in a Pareto solution. The gene and objective values are expressed as color and gray scales, respectively, after normalization. The gene values normalize using maximum and minimum values in all genes of Pareto solution, and in each gene, respectively. The objective function values normalize using maximum and minimum values in each objective function. To show the effectiveness of the proposed method, we apply the proposed method to benchmark problems. We easily found the relationship between the gene and objective functions values.

Original languageEnglish
Title of host publicationProceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2011
Pages267-272
Number of pages6
DOIs
Publication statusPublished - 2011 Dec 1
Event14th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2011 - Crete, Greece
Duration: 2011 Jun 222011 Jun 24

Publication series

NameProceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2011

Other

Other14th IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2011
CountryGreece
CityCrete
Period11/6/2211/6/24

Keywords

  • Information visualization
  • Multi-objective optimization
  • Pseudo color
  • Real-coded genetic algorithm

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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  • Cite this

    Ito, S. I., Mitsukura, Y., Saito, T., Sato, K., & Fujisawa, S. (2011). Visualization on pareto solutions in multi-objective optimization. In Proceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2011 (pp. 267-272). (Proceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2011). https://doi.org/10.2316/P.2011.716-050