Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/14653

TitleMulti-objective memetic algorithm : comparing artificial neural networks and pattern search filter method approaches
Author(s)Costa, M. Fernanda P.
Gaspar-Cunha, A.
Mendes, F.
KeywordsMemetic algorithms
Filter method
Multi-objective optimization
Artificial neural networks
Issue dateMar-2011
PublisherBlackwell Publishing
JournalInternational Transactions in Operational Research
Abstract(s)In this work, two methodologies to reduce the computation time of expensive multi-objective optimization problems are compared. These methodologies consist of the hybridization of a multi-objective evolutionary algorithm (MOEA) with local search procedures. First, an inverse artificial neural network proposed previously, consisting of mapping the decision variables into the multiple objectives to be optimized in order to generate improved solutions on certain generations of the MOEA, is presented. Second, a new approach based on a pattern search filter method is proposed in order to perform a local search around certain solutions selected previously from the Pareto frontier. The results obtained, by the application of both methodologies to difficult test problems, indicate a good performance of the approaches proposed.
TypeArticle
URIhttp://hdl.handle.net/1822/14653
DOI10.1111/j.1475-3995.2010.00782.x
ISSN1475-3995
Publisher versionhttp://onlinelibrary.wiley.com/doi/10.1111/j.1475-3995.2010.00782.x/abstract
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals
IPC - Artigos em revistas científicas internacionais com arbitragem

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