MOEA/D for constrained multiobjective optimization: some preliminary experimental results

Muhammad Asif Jan; Qingfu Zhang

doi:10.1109/UKCI.2010.5625585

MOEA/D for constrained multiobjective optimization: some preliminary experimental results

Muhammad Asif Jan, Qingfu Zhang

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

98 Citations (Scopus)

Abstract

This paper modifies the replacement and update scheme in MOEA/D-DE developed in [1] for dealing with constraints in multiobjective optimization problems. The modified scheme introduces a penalty function to penalize infeasible solutions. The penalty function uses a threshold to control the amount of penalty to infeasible solutions. Experimental results have shown that this penalty method is very promising.

Original language	English
Title of host publication	2010 UK Workshop on Computational Intelligence, UKCI 2010
DOIs	https://doi.org/10.1109/UKCI.2010.5625585
Publication status	Published - 2010
Externally published	Yes
Event	2010 UK Workshop on Computational Intelligence, UKCI 2010 - Colchester, United Kingdom Duration: 8 Sept 2010 → 10 Sept 2010

Conference

Conference	2010 UK Workshop on Computational Intelligence, UKCI 2010
Country/Territory	United Kingdom
City	Colchester
Period	8/09/10 → 10/09/10

Access Output

10.1109/UKCI.2010.5625585

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@inproceedings{bbc1c31f6be44f5ebd90ea69ea34b57e,

title = "MOEA/D for constrained multiobjective optimization: some preliminary experimental results",

abstract = "This paper modifies the replacement and update scheme in MOEA/D-DE developed in [1] for dealing with constraints in multiobjective optimization problems. The modified scheme introduces a penalty function to penalize infeasible solutions. The penalty function uses a threshold to control the amount of penalty to infeasible solutions. Experimental results have shown that this penalty method is very promising.",

author = "Jan, {Muhammad Asif} and Qingfu Zhang",

year = "2010",

doi = "10.1109/UKCI.2010.5625585",

language = "English",

isbn = "9781424487752",

booktitle = "2010 UK Workshop on Computational Intelligence, UKCI 2010",

note = "2010 UK Workshop on Computational Intelligence, UKCI 2010 ; Conference date: 08-09-2010 Through 10-09-2010",

}

TY - GEN

T1 - MOEA/D for constrained multiobjective optimization

T2 - 2010 UK Workshop on Computational Intelligence, UKCI 2010

AU - Jan, Muhammad Asif

AU - Zhang, Qingfu

PY - 2010

Y1 - 2010

N2 - This paper modifies the replacement and update scheme in MOEA/D-DE developed in [1] for dealing with constraints in multiobjective optimization problems. The modified scheme introduces a penalty function to penalize infeasible solutions. The penalty function uses a threshold to control the amount of penalty to infeasible solutions. Experimental results have shown that this penalty method is very promising.

AB - This paper modifies the replacement and update scheme in MOEA/D-DE developed in [1] for dealing with constraints in multiobjective optimization problems. The modified scheme introduces a penalty function to penalize infeasible solutions. The penalty function uses a threshold to control the amount of penalty to infeasible solutions. Experimental results have shown that this penalty method is very promising.

UR - http://www.scopus.com/inward/record.url?scp=78650199816&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-78650199816&origin=recordpage

U2 - 10.1109/UKCI.2010.5625585

DO - 10.1109/UKCI.2010.5625585

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

SN - 9781424487752

BT - 2010 UK Workshop on Computational Intelligence, UKCI 2010

Y2 - 8 September 2010 through 10 September 2010

ER -

MOEA/D for constrained multiobjective optimization: some preliminary experimental results

Abstract

Conference

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this