On performance of decomposition-based MOEAs in noisy environment

Qin Chen; Cheng Fu; Qingfu Zhang

doi:10.1109/CEC.2015.7257317

On performance of decomposition-based MOEAs in noisy environment

Qin Chen
, Cheng Fu
, Qingfu Zhang

Department of Computer Science

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

2 Citations (Scopus)

Abstract

Real world optimization often involves noises and uncertainty. Most current research on evolutionary multiobjective optimization does not consider the effect of noise. This paper studies the performance of decomposition based multiobjective optimization evolutionary algorithm (MOEA/D) in noisy environment. Experiments are carried out to compare the performance of MOEA/D and NSGA II under different levels of noise in objective functions evaluation. Statistical analysis has been made to understand the behaviour of MOEA/D. Based on the comparison and analysis, we discuss possible improvement methods on MOEA/D for noisy optimization.

Original language	English
Title of host publication	2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
Publisher	IEEE
Pages	3412-3417
ISBN (Print)	9781479974924
DOIs	https://doi.org/10.1109/CEC.2015.7257317
Publication status	Published - 10 Sept 2015
Event	IEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan Duration: 25 May 2015 → 28 May 2015

Conference

Conference	IEEE Congress on Evolutionary Computation, CEC 2015
Place	Japan
City	Sendai
Period	25/05/15 → 28/05/15

Research Keywords

Bayesian probability
decomposition
evolutionary algorithm
Multiobjective optimization
noisy evaluation

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10.1109/CEC.2015.7257317

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title = "On performance of decomposition-based MOEAs in noisy environment",

abstract = "Real world optimization often involves noises and uncertainty. Most current research on evolutionary multiobjective optimization does not consider the effect of noise. This paper studies the performance of decomposition based multiobjective optimization evolutionary algorithm (MOEA/D) in noisy environment. Experiments are carried out to compare the performance of MOEA/D and NSGA II under different levels of noise in objective functions evaluation. Statistical analysis has been made to understand the behaviour of MOEA/D. Based on the comparison and analysis, we discuss possible improvement methods on MOEA/D for noisy optimization.",

keywords = "Bayesian probability, decomposition, evolutionary algorithm, Multiobjective optimization, noisy evaluation",

author = "Qin Chen and Cheng Fu and Qingfu Zhang",

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doi = "10.1109/CEC.2015.7257317",

language = "English",

isbn = "9781479974924",

pages = "3412--3417",

booktitle = "2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings",

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T1 - On performance of decomposition-based MOEAs in noisy environment

AU - Chen, Qin

AU - Fu, Cheng

AU - Zhang, Qingfu

PY - 2015/9/10

Y1 - 2015/9/10

N2 - Real world optimization often involves noises and uncertainty. Most current research on evolutionary multiobjective optimization does not consider the effect of noise. This paper studies the performance of decomposition based multiobjective optimization evolutionary algorithm (MOEA/D) in noisy environment. Experiments are carried out to compare the performance of MOEA/D and NSGA II under different levels of noise in objective functions evaluation. Statistical analysis has been made to understand the behaviour of MOEA/D. Based on the comparison and analysis, we discuss possible improvement methods on MOEA/D for noisy optimization.

AB - Real world optimization often involves noises and uncertainty. Most current research on evolutionary multiobjective optimization does not consider the effect of noise. This paper studies the performance of decomposition based multiobjective optimization evolutionary algorithm (MOEA/D) in noisy environment. Experiments are carried out to compare the performance of MOEA/D and NSGA II under different levels of noise in objective functions evaluation. Statistical analysis has been made to understand the behaviour of MOEA/D. Based on the comparison and analysis, we discuss possible improvement methods on MOEA/D for noisy optimization.

KW - Bayesian probability

KW - decomposition

KW - evolutionary algorithm

KW - Multiobjective optimization

KW - noisy evaluation

UR - https://www.scopus.com/pages/publications/84963511180

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84963511180&origin=recordpage

U2 - 10.1109/CEC.2015.7257317

DO - 10.1109/CEC.2015.7257317

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

SN - 9781479974924

SP - 3412

EP - 3417

BT - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

PB - IEEE

T2 - IEEE Congress on Evolutionary Computation, CEC 2015

Y2 - 25 May 2015 through 28 May 2015

ER -

On performance of decomposition-based MOEAs in noisy environment

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