Online objective reduction for many-objective optimization problems

Yiu Ming CHEUNG; Fangqing Gu

doi:10.1109/CEC.2014.6900548

Online objective reduction for many-objective optimization problems

Yiu Ming CHEUNG^*, Fangqing Gu

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

14 Citations (Scopus)

Abstract

For many-objective optimization problems, i.e. the number of objectives is greater than three, the performance of most of the existing Evolutionary Multi-objective Optimization algorithms will deteriorate to a certain degree. It is therefore desirable to reduce many objectives to fewer essential objectives, if applicable. Currently, most of the existing objective reduction methods are based on objective selection, whose computational process is, however, laborious. In this paper, we will propose an online objective reduction method based on objective extraction for the many-objective optimization problems. It formulates the essential objective as a linear combination of the original objectives with the combination weights determined based on the correlations of each pair of the essential objectives. Subsequently, we will integrate it into NSGA-II. Numerical studies have show the efficacy of the proposed approach.

Original language	English
Title of host publication	Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
Publisher	IEEE
Pages	1165-1171
Number of pages	7
ISBN (Electronic)	9781479914883
DOIs	https://doi.org/10.1109/CEC.2014.6900548
Publication status	Published - 16 Sept 2014
Event	2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014

Publication series

Name	Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Conference

Conference	2014 IEEE Congress on Evolutionary Computation, CEC 2014
Country/Territory	China
City	Beijing
Period	6/07/14 → 11/07/14

Scopus Subject Areas

Artificial Intelligence
Computational Theory and Mathematics
Theoretical Computer Science

Access to Document

10.1109/CEC.2014.6900548

Cite this

@inproceedings{6578854f9e5d4a0b9c037d9f2753a798,

title = "Online objective reduction for many-objective optimization problems",

abstract = "For many-objective optimization problems, i.e. the number of objectives is greater than three, the performance of most of the existing Evolutionary Multi-objective Optimization algorithms will deteriorate to a certain degree. It is therefore desirable to reduce many objectives to fewer essential objectives, if applicable. Currently, most of the existing objective reduction methods are based on objective selection, whose computational process is, however, laborious. In this paper, we will propose an online objective reduction method based on objective extraction for the many-objective optimization problems. It formulates the essential objective as a linear combination of the original objectives with the combination weights determined based on the correlations of each pair of the essential objectives. Subsequently, we will integrate it into NSGA-II. Numerical studies have show the efficacy of the proposed approach.",

author = "CHEUNG, {Yiu Ming} and Fangqing Gu",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE. Copyright: Copyright 2015 Elsevier B.V., All rights reserved.; 2014 IEEE Congress on Evolutionary Computation, CEC 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

year = "2014",

month = sep,

day = "16",

doi = "10.1109/CEC.2014.6900548",

language = "English",

series = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014",

publisher = "IEEE",

pages = "1165--1171",

booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014",

address = "United States",

}

CHEUNG, YM & Gu, F 2014, Online objective reduction for many-objective optimization problems. in Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014., 6900548, Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, IEEE, pp. 1165-1171, 2014 IEEE Congress on Evolutionary Computation, CEC 2014, Beijing, China, 6/07/14. https://doi.org/10.1109/CEC.2014.6900548

Online objective reduction for many-objective optimization problems. / CHEUNG, Yiu Ming; Gu, Fangqing.
Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. IEEE, 2014. p. 1165-1171 6900548 (Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Online objective reduction for many-objective optimization problems

AU - CHEUNG, Yiu Ming

AU - Gu, Fangqing

PY - 2014/9/16

Y1 - 2014/9/16

N2 - For many-objective optimization problems, i.e. the number of objectives is greater than three, the performance of most of the existing Evolutionary Multi-objective Optimization algorithms will deteriorate to a certain degree. It is therefore desirable to reduce many objectives to fewer essential objectives, if applicable. Currently, most of the existing objective reduction methods are based on objective selection, whose computational process is, however, laborious. In this paper, we will propose an online objective reduction method based on objective extraction for the many-objective optimization problems. It formulates the essential objective as a linear combination of the original objectives with the combination weights determined based on the correlations of each pair of the essential objectives. Subsequently, we will integrate it into NSGA-II. Numerical studies have show the efficacy of the proposed approach.

AB - For many-objective optimization problems, i.e. the number of objectives is greater than three, the performance of most of the existing Evolutionary Multi-objective Optimization algorithms will deteriorate to a certain degree. It is therefore desirable to reduce many objectives to fewer essential objectives, if applicable. Currently, most of the existing objective reduction methods are based on objective selection, whose computational process is, however, laborious. In this paper, we will propose an online objective reduction method based on objective extraction for the many-objective optimization problems. It formulates the essential objective as a linear combination of the original objectives with the combination weights determined based on the correlations of each pair of the essential objectives. Subsequently, we will integrate it into NSGA-II. Numerical studies have show the efficacy of the proposed approach.

UR - http://www.scopus.com/inward/record.url?scp=84908587583&partnerID=8YFLogxK

U2 - 10.1109/CEC.2014.6900548

DO - 10.1109/CEC.2014.6900548

M3 - Conference proceeding

AN - SCOPUS:84908587583

T3 - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

SP - 1165

EP - 1171

BT - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

PB - IEEE

T2 - 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Y2 - 6 July 2014 through 11 July 2014

ER -

Online objective reduction for many-objective optimization problems

Abstract

Publication series

Conference

Scopus Subject Areas

Access to Document

Other files and links

Fingerprint

Cite this