Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/67156

TitleA Scalarized Augmented Lagrangian Algorithm (SCAL) for multi-objective optimization constrained problems
Author(s)Costa, Lino
Espírito Santo, I. A. C. P.
Oliveira, Pedro Nuno Ferreira Pinto
KeywordsAugmented Lagrangian
Augmented Weighted Tchebycheff
Multi-objective Constrained Optimization
Pattern Search
Issue date1-Jan-2018
PublisherSCITEPRESS
JournalICORES
Abstract(s)Science and Technology Publications, Lda. All rights reserved. In this paper, a methodology to solve constrained multi-objective problems is presented, using an Augmented Lagrangian technique to deal with the constraints and the Augmented Weighted Tchebycheff method to tackle the multi-objective problem and find the Pareto Frontier. We present the algorithm, as well as some preliminary results that seem very promising when compared to previous state-of-the- art work. As far as we know, the idea of incorporating an Augmented Lagrangian in multi-objective optimization is rarely used so, the obtained results are very encouraging to pursuit further in this line of investigation, namely with the tuning of the Augmented Lagrangian parameters as well as testing other algorithms to solve the subproblems or to handle the multi-objective problems. It is also our intention to investigate the resolution of problems with three or more objectives.
TypeConference paper
URIhttp://hdl.handle.net/1822/67156
ISBN978-989-758-285-1
ISSN2184-4372
Publisher versionhttps://www.scitepress.org/PublicationsDetail.aspx?ID=sYE4sd48eyc=&t=1
Peer-Reviewedyes
AccessRestricted access (Author)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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