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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Elsevier in its journal European Journal of Operational Research.
Volume (Year): 205 (2010)
Issue (Month): 2 (September)
Contact details of provider:
Web page: http://www.elsevier.com/locate/eor
Multiple objective programming Parallel computing Multiple reference point approach Evolutionary computations Bi-objective flow-shop scheduling;
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- 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.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If references are entirely missing, you can add them using this form.