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

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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Bibliographic Details
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
Subjects:
DRNTU::Engineering::Electrical and electronic engineering
Online Access:https://hdl.handle.net/10356/85254
http://hdl.handle.net/10220/16502
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Institution: Nanyang Technological University
Language: English
Description
Summary: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.

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