An external archive guided multiobjective evolutionary approach based on decomposition for continuous optimization

Yexing Li; Xinye Cai; Zhun Fan; Qingfu Zhang

doi:10.1109/CEC.2014.6900340

An external archive guided multiobjective evolutionary approach based on decomposition for continuous optimization

Yexing Li
, Xinye Cai
, Zhun Fan
, Qingfu Zhang

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

2 Citations (Scopus)

Abstract

In this paper, we propose a decomposition based multiobjective evolutionary algorithm that extracts information from an external archive to guide the evolutionary search for continuous optimization problem. The proposed algorithm used a mechanism to identify the promising regions(subproblems) through learning information from the external archive to guide evolutionary search process. In order to demonstrate the performance of the algorithm, we conduct experiments to compare it with other decomposition based approaches. The results validate that our proposed algorithm is very competitive.

Original language	English
Title of host publication	Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
Publisher	IEEE
Pages	1124-1130
ISBN (Print)	9781479914883
DOIs	https://doi.org/10.1109/CEC.2014.6900340
Publication status	Published - 16 Sept 2014
Externally published	Yes
Event	2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014

Conference

Conference	2014 IEEE Congress on Evolutionary Computation, CEC 2014
Place	China
City	Beijing
Period	6/07/14 → 11/07/14

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10.1109/CEC.2014.6900340

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@inproceedings{d556aa58dd684fc7b1197de43fa260fc,

title = "An external archive guided multiobjective evolutionary approach based on decomposition for continuous optimization",

abstract = "In this paper, we propose a decomposition based multiobjective evolutionary algorithm that extracts information from an external archive to guide the evolutionary search for continuous optimization problem. The proposed algorithm used a mechanism to identify the promising regions(subproblems) through learning information from the external archive to guide evolutionary search process. In order to demonstrate the performance of the algorithm, we conduct experiments to compare it with other decomposition based approaches. The results validate that our proposed algorithm is very competitive.",

author = "Yexing Li and Xinye Cai and Zhun Fan and Qingfu Zhang",

year = "2014",

month = sep,

day = "16",

doi = "10.1109/CEC.2014.6900340",

language = "English",

isbn = "9781479914883",

pages = "1124--1130",

booktitle = "Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014",

publisher = "IEEE",

address = "United States",

note = "2014 IEEE Congress on Evolutionary Computation, CEC 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

}

Li, Y, Cai, X, Fan, Z & Zhang, Q 2014, An external archive guided multiobjective evolutionary approach based on decomposition for continuous optimization. in Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014., 6900340, IEEE, pp. 1124-1130, 2014 IEEE Congress on Evolutionary Computation, CEC 2014, Beijing, China, 6/07/14. https://doi.org/10.1109/CEC.2014.6900340

An external archive guided multiobjective evolutionary approach based on decomposition for continuous optimization. / Li, Yexing; Cai, Xinye; Fan, Zhun et al.
Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. IEEE, 2014. p. 1124-1130 6900340.

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

TY - GEN

T1 - An external archive guided multiobjective evolutionary approach based on decomposition for continuous optimization

AU - Li, Yexing

AU - Cai, Xinye

AU - Fan, Zhun

AU - Zhang, Qingfu

PY - 2014/9/16

Y1 - 2014/9/16

N2 - In this paper, we propose a decomposition based multiobjective evolutionary algorithm that extracts information from an external archive to guide the evolutionary search for continuous optimization problem. The proposed algorithm used a mechanism to identify the promising regions(subproblems) through learning information from the external archive to guide evolutionary search process. In order to demonstrate the performance of the algorithm, we conduct experiments to compare it with other decomposition based approaches. The results validate that our proposed algorithm is very competitive.

AB - In this paper, we propose a decomposition based multiobjective evolutionary algorithm that extracts information from an external archive to guide the evolutionary search for continuous optimization problem. The proposed algorithm used a mechanism to identify the promising regions(subproblems) through learning information from the external archive to guide evolutionary search process. In order to demonstrate the performance of the algorithm, we conduct experiments to compare it with other decomposition based approaches. The results validate that our proposed algorithm is very competitive.

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DO - 10.1109/CEC.2014.6900340

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781479914883

SP - 1124

EP - 1130

BT - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

PB - IEEE

T2 - 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Y2 - 6 July 2014 through 11 July 2014

ER -

An external archive guided multiobjective evolutionary approach based on decomposition for continuous optimization

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