A parallel multiple reference point approach for multi-objective optimization
AbstractThis paper presents a multiple reference point approach for multi-objective optimization problems of discrete and combinatorial nature. When approximating the Pareto Frontier, multiple reference points can be used instead of traditional techniques. These multiple reference points can easily be implemented in a parallel algorithmic framework. The reference points can be uniformly distributed within a region that covers the Pareto Frontier. An evolutionary algorithm is based on an achievement scalarizing function that does not impose any restrictions with respect to the location of the reference points in the objective space. Computational experiments are performed on a bi-objective flow-shop scheduling problem. Results, quality measures as well as a statistical analysis are reported in the paper.
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Bibliographic InfoArticle provided by Elsevier in its journal European Journal of Operational Research.
Volume (Year): 205 (2010)
Issue (Month): 2 (September)
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Web page: http://www.elsevier.com/locate/eor
Multiple objective programming Parallel computing Multiple reference point approach Evolutionary computations Bi-objective flow-shop scheduling;
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- Liefooghe, Arnaud & Jourdan, Laetitia & Talbi, El-Ghazali, 2011. "A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO," European Journal of Operational Research, Elsevier, vol. 209(2), pages 104-112, March.
- Liang, Wen Yau & Huang, Chun-Che & Lin, Yin-Chen & Chang, Tsun Hsien & Shih, Meng Hao, 2013. "The multi-objective label correcting algorithm for supply chain modeling," International Journal of Production Economics, Elsevier, vol. 142(1), pages 172-178.
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