Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/37046

TitleDDMOA: Descent Directions based Multiobjective Algorithm
Author(s)Denysiuk, Roman
Costa, L.
Espírito Santo, I. A. C. P.
KeywordsMultiobjective optimization
Evolutionary algorithms
Pattern search
Performance assessment
Issue date2012
Abstract(s)Hybridization of local search based algorithms with evolutionary algorithms is still an under-explored research area in multiobjective optimization. In this paper, we propose a new multiobjective algorithm based on a local search method. The main idea is to generate new non-dominated solutions by adding to a parent solution a linear combination of descent directions of the objective functions. Additionally, a strategy based on subpopulations is implemented to avoid the direct computation of descent directions for the entire population. The evaluation of the proposed algorithm is performed on a set of benchmark test problems allowing a comparison with the most representative stateof- the-art multiobjective algorithms. The results show that the proposed approach is highly competitive in terms of the quality of non-dominated solutions, robustness and the computational efficiency.
TypeConference paper
URIhttp://hdl.handle.net/1822/37046
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CAlg - Artigos em livros de atas/Papers in proceedings

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