Noise-induced features in robust multi-objective optimization problems

C. K. Goh; K. C. Tan; C. Y. Cheong; Y. S. Ong

doi:10.1109/CEC.2007.4424521

Noise-induced features in robust multi-objective optimization problems

C. K. Goh
, K. C. Tan
, C. Y. Cheong
, Y. S. Ong

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

6 Citations (Scopus)

Abstract

Apart from the need to satisfy several competing objectives, many real-world applications are also sensitive to decision or environmental parameter variation which results in large or unacceptable performance variation. While evolutionary optimization techniques have several advantages over operational research methods for robust optimization, it is rarely studied by the evolutionary multi-objective (MO) optimization community. This paper addresses the issue of robust MO optimization by presenting a robust continuous MO test suite with features of noise-induced solution space, fitness landscape and decision space variation. The work presented in this paper should encourage further studies and the development of more effective algorithms for robust MO optimization. © 2007 IEEE.

Original language	English
Title of host publication	2007 IEEE Congress on Evolutionary Computation
Publisher	IEEE
Pages	568-575
ISBN (Electronic)	978-1-4244-1340-9
ISBN (Print)	978-1-4244-1339-3
DOIs	https://doi.org/10.1109/CEC.2007.4424521
Publication status	Published - Sept 2007
Externally published	Yes
Event	2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore Duration: 25 Sept 2007 → 28 Sept 2007

Conference

Conference	2007 IEEE Congress on Evolutionary Computation, CEC 2007
Place	Singapore
Period	25/09/07 → 28/09/07

Research Keywords

Evolutionary algorithms
Multi-objective optimization
Robust solutions
Robust test functions

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10.1109/CEC.2007.4424521

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@inproceedings{b058f3bd4f3f422ba1d357e2d891f827,

title = "Noise-induced features in robust multi-objective optimization problems",

abstract = "Apart from the need to satisfy several competing objectives, many real-world applications are also sensitive to decision or environmental parameter variation which results in large or unacceptable performance variation. While evolutionary optimization techniques have several advantages over operational research methods for robust optimization, it is rarely studied by the evolutionary multi-objective (MO) optimization community. This paper addresses the issue of robust MO optimization by presenting a robust continuous MO test suite with features of noise-induced solution space, fitness landscape and decision space variation. The work presented in this paper should encourage further studies and the development of more effective algorithms for robust MO optimization. {\textcopyright} 2007 IEEE.",

keywords = "Evolutionary algorithms, Multi-objective optimization, Robust solutions, Robust test functions",

author = "Goh, \{C. K.\} and Tan, \{K. C.\} and Cheong, \{C. Y.\} and Ong, \{Y. S.\}",

year = "2007",

month = sep,

doi = "10.1109/CEC.2007.4424521",

language = "English",

isbn = "978-1-4244-1339-3",

pages = "568--575",

booktitle = "2007 IEEE Congress on Evolutionary Computation",

publisher = "IEEE",

address = "United States",

note = "2007 IEEE Congress on Evolutionary Computation, CEC 2007 ; Conference date: 25-09-2007 Through 28-09-2007",

}

TY - GEN

T1 - Noise-induced features in robust multi-objective optimization problems

AU - Goh, C. K.

AU - Tan, K. C.

AU - Cheong, C. Y.

AU - Ong, Y. S.

PY - 2007/9

Y1 - 2007/9

N2 - Apart from the need to satisfy several competing objectives, many real-world applications are also sensitive to decision or environmental parameter variation which results in large or unacceptable performance variation. While evolutionary optimization techniques have several advantages over operational research methods for robust optimization, it is rarely studied by the evolutionary multi-objective (MO) optimization community. This paper addresses the issue of robust MO optimization by presenting a robust continuous MO test suite with features of noise-induced solution space, fitness landscape and decision space variation. The work presented in this paper should encourage further studies and the development of more effective algorithms for robust MO optimization. © 2007 IEEE.

AB - Apart from the need to satisfy several competing objectives, many real-world applications are also sensitive to decision or environmental parameter variation which results in large or unacceptable performance variation. While evolutionary optimization techniques have several advantages over operational research methods for robust optimization, it is rarely studied by the evolutionary multi-objective (MO) optimization community. This paper addresses the issue of robust MO optimization by presenting a robust continuous MO test suite with features of noise-induced solution space, fitness landscape and decision space variation. The work presented in this paper should encourage further studies and the development of more effective algorithms for robust MO optimization. © 2007 IEEE.

KW - Evolutionary algorithms

KW - Multi-objective optimization

KW - Robust solutions

KW - Robust test functions

UR - https://www.scopus.com/pages/publications/46049097680

UR - https://www.scopus.com/pages/publications/46049097680#tab=impact

U2 - 10.1109/CEC.2007.4424521

DO - 10.1109/CEC.2007.4424521

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-4244-1339-3

SP - 568

EP - 575

BT - 2007 IEEE Congress on Evolutionary Computation

PB - IEEE

T2 - 2007 IEEE Congress on Evolutionary Computation, CEC 2007

Y2 - 25 September 2007 through 28 September 2007

ER -

Noise-induced features in robust multi-objective optimization problems

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