A hybrid algorithm based on MOEA/D and local search for multiobjective optimization

Man Fai Leung; Sin Chun Ng

doi:10.1109/CEC48606.2020.9185741

A hybrid algorithm based on MOEA/D and local search for multiobjective optimization

Man Fai Leung
, Sin Chun Ng

Hong Kong Metropolitan University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Citations (Scopus)

Abstract

A hybrid algorithm is proposed for multiobjective optimization in this paper. The proposed algorithm consists of multiobjective evolutionary algorithm based on decomposition (MOEA/D) and recurrent neural network, where MOEA/D is for global search while recurrent neural network is for local search. The performance of the proposed algorithm is compared with other three multi-objective algorithms in terms of hypervolume and inverted generational distance. The performance investigation shows that the proposed algorithm generally outperforms the compared algorithms.

Original language	English
Title of host publication	2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings
ISBN (Electronic)	9781728169293
DOIs	https://doi.org/10.1109/CEC48606.2020.9185741
Publication status	Published - Jul 2020
Event	2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Virtual, Glasgow, United Kingdom Duration: 19 Jul 2020 → 24 Jul 2020

Publication series

Name	2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

Conference

Conference	2020 IEEE Congress on Evolutionary Computation, CEC 2020
Country/Territory	United Kingdom
City	Virtual, Glasgow
Period	19/07/20 → 24/07/20

Keywords

evolutionary algorithm
MOEA/D
multiobjective optimization
Pareto-optimal solution
recurrent neural network

Access to Document

10.1109/CEC48606.2020.9185741

Cite this

@inproceedings{5c5281c9c5bc4d7bbbc3ad6fbba2c45c,

title = "A hybrid algorithm based on MOEA/D and local search for multiobjective optimization",

abstract = "A hybrid algorithm is proposed for multiobjective optimization in this paper. The proposed algorithm consists of multiobjective evolutionary algorithm based on decomposition (MOEA/D) and recurrent neural network, where MOEA/D is for global search while recurrent neural network is for local search. The performance of the proposed algorithm is compared with other three multi-objective algorithms in terms of hypervolume and inverted generational distance. The performance investigation shows that the proposed algorithm generally outperforms the compared algorithms.",

keywords = "evolutionary algorithm, MOEA/D, multiobjective optimization, Pareto-optimal solution, recurrent neural network",

author = "Leung, \{Man Fai\} and Ng, \{Sin Chun\}",

note = "Funding Information: The work described in this paper was fully supported by the Open University of Hong Kong Research Grant (No. R5083). Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE Congress on Evolutionary Computation, CEC 2020 ; Conference date: 19-07-2020 Through 24-07-2020",

year = "2020",

month = jul,

doi = "10.1109/CEC48606.2020.9185741",

language = "English",

series = "2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings",

booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings",

}

Leung, MF & Ng, SC 2020, A hybrid algorithm based on MOEA/D and local search for multiobjective optimization. in 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings., 9185741, 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings, 2020 IEEE Congress on Evolutionary Computation, CEC 2020, Virtual, Glasgow, United Kingdom, 19/07/20. https://doi.org/10.1109/CEC48606.2020.9185741

A hybrid algorithm based on MOEA/D and local search for multiobjective optimization. / Leung, Man Fai; Ng, Sin Chun.
2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. 2020. 9185741 (2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Leung, Man Fai

AU - Ng, Sin Chun

PY - 2020/7

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N2 - A hybrid algorithm is proposed for multiobjective optimization in this paper. The proposed algorithm consists of multiobjective evolutionary algorithm based on decomposition (MOEA/D) and recurrent neural network, where MOEA/D is for global search while recurrent neural network is for local search. The performance of the proposed algorithm is compared with other three multi-objective algorithms in terms of hypervolume and inverted generational distance. The performance investigation shows that the proposed algorithm generally outperforms the compared algorithms.

AB - A hybrid algorithm is proposed for multiobjective optimization in this paper. The proposed algorithm consists of multiobjective evolutionary algorithm based on decomposition (MOEA/D) and recurrent neural network, where MOEA/D is for global search while recurrent neural network is for local search. The performance of the proposed algorithm is compared with other three multi-objective algorithms in terms of hypervolume and inverted generational distance. The performance investigation shows that the proposed algorithm generally outperforms the compared algorithms.

KW - evolutionary algorithm

KW - MOEA/D

KW - multiobjective optimization

KW - Pareto-optimal solution

KW - recurrent neural network

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U2 - 10.1109/CEC48606.2020.9185741

DO - 10.1109/CEC48606.2020.9185741

M3 - Conference contribution

AN - SCOPUS:85092021893

T3 - 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

BT - 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings

T2 - 2020 IEEE Congress on Evolutionary Computation, CEC 2020

Y2 - 19 July 2020 through 24 July 2020

ER -

A hybrid algorithm based on MOEA/D and local search for multiobjective optimization

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Keywords

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