An Empirical Study of Dynamic Triobjective Optimisation Problems

Shouyong Jiang; Marcus Kaiser; Shuzhen Wan; Jinglei Guo; Shengxiang Yang; Natalio Krasnogor

doi:10.1109/CEC.2018.8477667

An Empirical Study of Dynamic Triobjective Optimisation Problems

Shouyong Jiang, Marcus Kaiser, Shuzhen Wan, Jinglei Guo, Shengxiang Yang, Natalio Krasnogor

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

5 Citations (Scopus)

Abstract

Dynamic multiobjective optimisation deals with multiobjective problems whose objective functions, search spaces, or constraints are time-varying during the optimisation process. Due to wide presence in real-world applications, dynamic multiobjective problems (DMOPs) have been increasingly studied in recent years. Whilst most studies concentrated on DMOPs with only two objectives, there is little work on more objectives. This paper presents an empirical investigation of evolutionary algorithms for three-objective dynamic problems. Experimental studies show that all the evolutionary algorithms tested in this paper encounter performance degradedness to some extent. Amongst these algorithms, the multipopulation based change handling mechanism is generally more robust for a larger number of objectives, but has difficulty in deal with time-varying deceptive characteristics.

Original language	English
Title of host publication	2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509060177
DOIs	https://doi.org/10.1109/CEC.2018.8477667
Publication status	Published - 28 Sept 2018
Event	2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018

Publication series

Name	2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings

Conference

Conference	2018 IEEE Congress on Evolutionary Computation, CEC 2018
Country/Territory	Brazil
City	Rio de Janeiro
Period	8/07/18 → 13/07/18

Bibliographical note

Funding Information:
This work was supported in part by the Engineering and Physical Sciences Research Council of U.K. under the project “Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies (EP/N031962/1)” , and in part by the National Natural Science Foundation of China under Grant 61673331.

Publisher Copyright:
© 2018 IEEE.

Access to Document

10.1109/CEC.2018.8477667

Cite this

Jiang, S., Kaiser, M., Wan, S., Guo, J., Yang, S., & Krasnogor, N. (2018). An Empirical Study of Dynamic Triobjective Optimisation Problems. In 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings Article 8477667 (2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2018.8477667

An Empirical Study of Dynamic Triobjective Optimisation Problems. / Jiang, Shouyong; Kaiser, Marcus; Wan, Shuzhen et al.
2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8477667 (2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Jiang, S, Kaiser, M, Wan, S, Guo, J, Yang, S & Krasnogor, N 2018, An Empirical Study of Dynamic Triobjective Optimisation Problems. in 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings., 8477667, 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE Congress on Evolutionary Computation, CEC 2018, Rio de Janeiro, Brazil, 8/07/18. https://doi.org/10.1109/CEC.2018.8477667

@inproceedings{29bdafc3fa8a45ad8138dede24a89910,

title = "An Empirical Study of Dynamic Triobjective Optimisation Problems",

abstract = "Dynamic multiobjective optimisation deals with multiobjective problems whose objective functions, search spaces, or constraints are time-varying during the optimisation process. Due to wide presence in real-world applications, dynamic multiobjective problems (DMOPs) have been increasingly studied in recent years. Whilst most studies concentrated on DMOPs with only two objectives, there is little work on more objectives. This paper presents an empirical investigation of evolutionary algorithms for three-objective dynamic problems. Experimental studies show that all the evolutionary algorithms tested in this paper encounter performance degradedness to some extent. Amongst these algorithms, the multipopulation based change handling mechanism is generally more robust for a larger number of objectives, but has difficulty in deal with time-varying deceptive characteristics.",

author = "Shouyong Jiang and Marcus Kaiser and Shuzhen Wan and Jinglei Guo and Shengxiang Yang and Natalio Krasnogor",

note = "Funding Information: This work was supported in part by the Engineering and Physical Sciences Research Council of U.K. under the project “Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies (EP/N031962/1)” , and in part by the National Natural Science Foundation of China under Grant 61673331. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE Congress on Evolutionary Computation, CEC 2018 ; Conference date: 08-07-2018 Through 13-07-2018",

year = "2018",

month = sep,

day = "28",

doi = "10.1109/CEC.2018.8477667",

language = "English",

series = "2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings",

address = "United States",

}

TY - GEN

T1 - An Empirical Study of Dynamic Triobjective Optimisation Problems

AU - Jiang, Shouyong

AU - Kaiser, Marcus

AU - Wan, Shuzhen

AU - Guo, Jinglei

AU - Yang, Shengxiang

AU - Krasnogor, Natalio

N1 - Funding Information: This work was supported in part by the Engineering and Physical Sciences Research Council of U.K. under the project “Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies (EP/N031962/1)” , and in part by the National Natural Science Foundation of China under Grant 61673331. Publisher Copyright: © 2018 IEEE.

PY - 2018/9/28

Y1 - 2018/9/28

N2 - Dynamic multiobjective optimisation deals with multiobjective problems whose objective functions, search spaces, or constraints are time-varying during the optimisation process. Due to wide presence in real-world applications, dynamic multiobjective problems (DMOPs) have been increasingly studied in recent years. Whilst most studies concentrated on DMOPs with only two objectives, there is little work on more objectives. This paper presents an empirical investigation of evolutionary algorithms for three-objective dynamic problems. Experimental studies show that all the evolutionary algorithms tested in this paper encounter performance degradedness to some extent. Amongst these algorithms, the multipopulation based change handling mechanism is generally more robust for a larger number of objectives, but has difficulty in deal with time-varying deceptive characteristics.

AB - Dynamic multiobjective optimisation deals with multiobjective problems whose objective functions, search spaces, or constraints are time-varying during the optimisation process. Due to wide presence in real-world applications, dynamic multiobjective problems (DMOPs) have been increasingly studied in recent years. Whilst most studies concentrated on DMOPs with only two objectives, there is little work on more objectives. This paper presents an empirical investigation of evolutionary algorithms for three-objective dynamic problems. Experimental studies show that all the evolutionary algorithms tested in this paper encounter performance degradedness to some extent. Amongst these algorithms, the multipopulation based change handling mechanism is generally more robust for a larger number of objectives, but has difficulty in deal with time-varying deceptive characteristics.

UR - http://www.scopus.com/inward/record.url?scp=85056252762&partnerID=8YFLogxK

U2 - 10.1109/CEC.2018.8477667

DO - 10.1109/CEC.2018.8477667

M3 - Published conference contribution

AN - SCOPUS:85056252762

T3 - 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings

BT - 2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE Congress on Evolutionary Computation, CEC 2018

Y2 - 8 July 2018 through 13 July 2018

ER -

An Empirical Study of Dynamic Triobjective Optimisation Problems

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this