Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has demonstrated superior performance by winning the multiobjective optimization algorithm competition at the CEC 2009. For effective performance of MOEA/D, neighborhood size (NS) parameter has to be tuned. In this letter, an...

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Main Authors: Suganthan, P. N., Zhao, Shi-Zheng., Zhang, Qing Fu.
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2013
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Online Access:https://hdl.handle.net/10356/85254
http://hdl.handle.net/10220/16502
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-852542020-03-07T13:57:27Z Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes Suganthan, P. N. Zhao, Shi-Zheng. Zhang, Qing Fu. School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has demonstrated superior performance by winning the multiobjective optimization algorithm competition at the CEC 2009. For effective performance of MOEA/D, neighborhood size (NS) parameter has to be tuned. In this letter, an ensemble of different NSs with online self-adaptation is proposed (ENS-MOEA/D) to overcome this shortcoming. Our experimental results on the CEC 2009 competition test instances show that an ensemble of different NSs with online self-adaptation yields superior performance over implementations with only one fixed NS. 2013-10-16T03:00:27Z 2019-12-06T16:00:28Z 2013-10-16T03:00:27Z 2019-12-06T16:00:28Z 2012 2012 Journal Article Zhao, S. Z., Suganthan, P. N., & Zhang, Q. F. (2012). Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes. IEEE transactions on evolutionary computation, 16(3), 442-446. https://hdl.handle.net/10356/85254 http://hdl.handle.net/10220/16502 10.1109/TEVC.2011.2166159 en IEEE transactions on evolutionary computation
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Suganthan, P. N.
Zhao, Shi-Zheng.
Zhang, Qing Fu.
Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes
description The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has demonstrated superior performance by winning the multiobjective optimization algorithm competition at the CEC 2009. For effective performance of MOEA/D, neighborhood size (NS) parameter has to be tuned. In this letter, an ensemble of different NSs with online self-adaptation is proposed (ENS-MOEA/D) to overcome this shortcoming. Our experimental results on the CEC 2009 competition test instances show that an ensemble of different NSs with online self-adaptation yields superior performance over implementations with only one fixed NS.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Suganthan, P. N.
Zhao, Shi-Zheng.
Zhang, Qing Fu.
format Article
author Suganthan, P. N.
Zhao, Shi-Zheng.
Zhang, Qing Fu.
author_sort Suganthan, P. N.
title Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes
title_short Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes
title_full Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes
title_fullStr Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes
title_full_unstemmed Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes
title_sort decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes
publishDate 2013
url https://hdl.handle.net/10356/85254
http://hdl.handle.net/10220/16502
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