A Performance Indicator for Reference-Point-Based Multiobjective Evolutionary Optimization

Zhanglu HOU; Shengxiang YANG; Juan ZOU; Jinhua ZHENG; Guo YU; Gan RUAN

doi:10.1109/SSCI.2018.8628834

A Performance Indicator for Reference-Point-Based Multiobjective Evolutionary Optimization

Zhanglu HOU, Shengxiang YANG, Juan ZOU^*, Jinhua ZHENG, Guo YU, Gan RUAN

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

13 Citations (Scopus)

Abstract

Aiming at the difficulty in evaluating preference-based evolutionary multiobjective optimization, this paper proposes a new performance indicator. The main idea is to project the preferred solutions onto a constructed hyperplane which is perpendicular to the vector from the reference (aspiration) point to the origin. And then the distance from preferred solutions to the origin and the standard deviation of distance from each mapping point to the nearest point will be calculated. The former is used to measure the convergence of the obtained solutions. The latter is utilized to assess the diversity of preferred solutions in the region of interest. The indicator is conducted to assess different algorithms on a series of benchmark problems with various features. The results show that the proposed indicator is able to properly evaluate the performance of preference-based multiobjective evolutionary algorithms.

Original language	English
Title of host publication	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
Editors	Suresh SUNDARAM
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1571-1578
Number of pages	8
ISBN (Electronic)	9781538692769
DOIs	https://doi.org/10.1109/SSCI.2018.8628834
Publication status	Published - 2 Jul 2018
Externally published	Yes

Publication series

Name	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Funding

This work was supported by the research projects: the National Natural Science Foundation of China under Grant Nos. 61502408, 61673331, 61379062 and 61403326, the Education Department Major Project of Hunan Province under Grant No. 17A212, the CERNET Innovation Project under Grant No. NGII20150302, the Natural Science Foundation of Hunan Province under Grant No. 14JJ2072, the Science and Technology Plan Project of Hunan Province under Grant No. 2016TP1020, the Provinces and Cities Joint Foundation Project under Grant No. 2017JJ4001.

Keywords

indicator
preference
reference point

Access to Document

10.1109/SSCI.2018.8628834

Cite this

HOU, Z., YANG, S., ZOU, J., ZHENG, J., YU, G., & RUAN, G. (2018). A Performance Indicator for Reference-Point-Based Multiobjective Evolutionary Optimization. In S. SUNDARAM (Ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 (pp. 1571-1578). Article 8628834 (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2018.8628834

HOU, Zhanglu ; YANG, Shengxiang ; ZOU, Juan et al. / A Performance Indicator for Reference-Point-Based Multiobjective Evolutionary Optimization. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. editor / Suresh SUNDARAM. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1571-1578 (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018).

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abstract = "Aiming at the difficulty in evaluating preference-based evolutionary multiobjective optimization, this paper proposes a new performance indicator. The main idea is to project the preferred solutions onto a constructed hyperplane which is perpendicular to the vector from the reference (aspiration) point to the origin. And then the distance from preferred solutions to the origin and the standard deviation of distance from each mapping point to the nearest point will be calculated. The former is used to measure the convergence of the obtained solutions. The latter is utilized to assess the diversity of preferred solutions in the region of interest. The indicator is conducted to assess different algorithms on a series of benchmark problems with various features. The results show that the proposed indicator is able to properly evaluate the performance of preference-based multiobjective evolutionary algorithms.",

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author = "Zhanglu HOU and Shengxiang YANG and Juan ZOU and Jinhua ZHENG and Guo YU and Gan RUAN",

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year = "2018",

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HOU, Z, YANG, S, ZOU, J, ZHENG, J, YU, G & RUAN, G 2018, A Performance Indicator for Reference-Point-Based Multiobjective Evolutionary Optimization. in S SUNDARAM (ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018., 8628834, Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1571-1578. https://doi.org/10.1109/SSCI.2018.8628834

A Performance Indicator for Reference-Point-Based Multiobjective Evolutionary Optimization. / HOU, Zhanglu; YANG, Shengxiang; ZOU, Juan et al.
Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. ed. / Suresh SUNDARAM. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1571-1578 8628834 (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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AU - RUAN, Gan

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AB - Aiming at the difficulty in evaluating preference-based evolutionary multiobjective optimization, this paper proposes a new performance indicator. The main idea is to project the preferred solutions onto a constructed hyperplane which is perpendicular to the vector from the reference (aspiration) point to the origin. And then the distance from preferred solutions to the origin and the standard deviation of distance from each mapping point to the nearest point will be calculated. The former is used to measure the convergence of the obtained solutions. The latter is utilized to assess the diversity of preferred solutions in the region of interest. The indicator is conducted to assess different algorithms on a series of benchmark problems with various features. The results show that the proposed indicator is able to properly evaluate the performance of preference-based multiobjective evolutionary algorithms.

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HOU Z, YANG S, ZOU J, ZHENG J, YU G, RUAN G. A Performance Indicator for Reference-Point-Based Multiobjective Evolutionary Optimization. In SUNDARAM S, editor, Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1571-1578. 8628834. (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018). doi: 10.1109/SSCI.2018.8628834

A Performance Indicator for Reference-Point-Based Multiobjective Evolutionary Optimization

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