A decomposition based multiobjective evolutionary algorithm with classification

Xi Lin; Qingfu Zhang; Sam Kwong

doi:10.1109/CEC.2016.7744206

A decomposition based multiobjective evolutionary algorithm with classification

^*Corresponding author for this work

Department of Computer Science

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

35 Citations (Scopus)

Abstract

This paper investigates how to use a pre-selection approach to improve the performance of the multiobjective evolutionary algorithm based on decomposition (MOEA/D). It proposes a novel MOEA/D algorithm with classification to serve this purpose. The proposed algorithm builds a classification model on the search space to filter all new generated solutions, and mainly evaluates those promising solutions for reducing real function evaluation costs during the search process. Experimental study on different test instances validates that the pre-selection approach can significantly improve the performance of a classical MOEA/D.

Original language	English
Title of host publication	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Publisher	IEEE
Pages	3292-3299
ISBN (Print)	9781509006229
DOIs	https://doi.org/10.1109/CEC.2016.7744206
Publication status	Published - 14 Nov 2016
Event	2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016

Publication series

Name	IEEE Congress on Evolutionary Computation
Publisher	IEEE

Conference

Conference	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Place	Canada
City	Vancouver
Period	24/07/16 → 29/07/16

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10.1109/CEC.2016.7744206

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@inproceedings{9da62235d75048929f6c1251684bc468,

title = "A decomposition based multiobjective evolutionary algorithm with classification",

abstract = "This paper investigates how to use a pre-selection approach to improve the performance of the multiobjective evolutionary algorithm based on decomposition (MOEA/D). It proposes a novel MOEA/D algorithm with classification to serve this purpose. The proposed algorithm builds a classification model on the search space to filter all new generated solutions, and mainly evaluates those promising solutions for reducing real function evaluation costs during the search process. Experimental study on different test instances validates that the pre-selection approach can significantly improve the performance of a classical MOEA/D.",

author = "Xi Lin and Qingfu Zhang and Sam Kwong",

year = "2016",

month = nov,

day = "14",

doi = "10.1109/CEC.2016.7744206",

language = "English",

isbn = "9781509006229",

series = "IEEE Congress on Evolutionary Computation",

publisher = "IEEE",

pages = "3292--3299",

booktitle = "2016 IEEE Congress on Evolutionary Computation, CEC 2016",

address = "United States",

note = "2016 IEEE Congress on Evolutionary Computation, CEC 2016 ; Conference date: 24-07-2016 Through 29-07-2016",

}

Lin, X , Zhang, Q & Kwong, S 2016, A decomposition based multiobjective evolutionary algorithm with classification. in 2016 IEEE Congress on Evolutionary Computation, CEC 2016., 7744206, IEEE Congress on Evolutionary Computation, IEEE, pp. 3292-3299, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canada, 24/07/16. https://doi.org/10.1109/CEC.2016.7744206

A decomposition based multiobjective evolutionary algorithm with classification. / Lin, Xi ; Zhang, Qingfu ; Kwong, Sam.
2016 IEEE Congress on Evolutionary Computation, CEC 2016. IEEE, 2016. p. 3292-3299 7744206 (IEEE Congress on Evolutionary Computation).

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

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AU - Zhang, Qingfu

AU - Kwong, Sam

PY - 2016/11/14

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N2 - This paper investigates how to use a pre-selection approach to improve the performance of the multiobjective evolutionary algorithm based on decomposition (MOEA/D). It proposes a novel MOEA/D algorithm with classification to serve this purpose. The proposed algorithm builds a classification model on the search space to filter all new generated solutions, and mainly evaluates those promising solutions for reducing real function evaluation costs during the search process. Experimental study on different test instances validates that the pre-selection approach can significantly improve the performance of a classical MOEA/D.

AB - This paper investigates how to use a pre-selection approach to improve the performance of the multiobjective evolutionary algorithm based on decomposition (MOEA/D). It proposes a novel MOEA/D algorithm with classification to serve this purpose. The proposed algorithm builds a classification model on the search space to filter all new generated solutions, and mainly evaluates those promising solutions for reducing real function evaluation costs during the search process. Experimental study on different test instances validates that the pre-selection approach can significantly improve the performance of a classical MOEA/D.

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BT - 2016 IEEE Congress on Evolutionary Computation, CEC 2016

PB - IEEE

T2 - 2016 IEEE Congress on Evolutionary Computation, CEC 2016

Y2 - 24 July 2016 through 29 July 2016

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A decomposition based multiobjective evolutionary algorithm with classification

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2016 IEEE Congress on Evolutionary Computation, CEC 2016

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A decomposition based multiobjective evolutionary algorithm with classification

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2016 IEEE Congress on Evolutionary Computation, CEC 2016

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