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

Xi LIN, Qingfu ZHANG, Sam KWONG

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

33 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
Number of pages	8
ISBN (Electronic)	9781509006236
ISBN (Print)	9781509006243
DOIs	https://doi.org/10.1109/CEC.2016.7744206
Publication status	Published - 14 Nov 2016
Externally published	Yes
Event	2016 IEEE Congress on Evolutionary Computation - Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016

Conference

Conference	2016 IEEE Congress on Evolutionary Computation
Abbreviated title	CEC 2016
Country/Territory	Canada
City	Vancouver
Period	24/07/16 → 29/07/16

Funding

This work was supported by the National Natural Science Foundation of China under Grants 61473241.

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10.1109/CEC.2016.7744206

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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.",

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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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M3 - Conference paper (refereed)

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PB - IEEE

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ER -

A decomposition based multiobjective evolutionary algorithm with classification

Abstract

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