Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/37048

TitleDDMOA2: Improved Descent Directions-Based Multiobjective Algorithm,
Author(s)Denysiuk, Roman
Costa, L.
Espírito Santo, I. A. C. P.
KeywordsMultiobjective optimization
Multiobjective evolutionary algorithms
Performance assessment
Issue date2013
Abstract(s)In this paper, we propose an improved version of descent direction-based multiobjective algorithm (DDMOA2). Significant modifications are introduced comparing with the originally proposed algorithm (DDMOA). DDMOA2 does not rely on the concept of Pareto dominance instead a scalarizing fitness assignment is used. Now, all population members have a probability of creating offspring. We define the concept of search matrix and population leaders for which local search is used to find descent directions. Moreover to improve efficiency, descent directions are found only for two randomly chosen objectives. The experimental study shows that the proposed approach outperforms the previous version of the algorithm with respect to the convergence to the Pareto optimal front and the diversity among obtained solutions, especially on three-objective test problems. At the same time, it provides highly competitive results with respect to other state-ofthe- art multiobjective optimizers.
TypeConference paper
URIhttp://hdl.handle.net/1822/37048
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

Files in This Item:
File Description SizeFormat 
d35.pdf
  Restricted access
142,45 kBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID