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Multiple reference point-based parallel evolutionary multiobjective optimization

José Figueira 1 Arnaud Liefooghe 2 El-Ghazali Talbi 2 Andrzej Wiersbicki 
1 ORCHIDS - Operations research for Complex HybrId Decision Sytems
LORIA - NSS - Department of Networks, Systems and Services
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : This 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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Submitted on : Wednesday, July 6, 2011 - 2:55:44 PM
Last modification on : Saturday, June 25, 2022 - 7:40:07 PM

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José Figueira, Arnaud Liefooghe, El-Ghazali Talbi, Andrzej Wiersbicki. Multiple reference point-based parallel evolutionary multiobjective optimization. META 2010 - International Conference on Metaheuristics and Nature Inspired Computing, Oct 2010, Tunis, Tunisia. ⟨10.1016/j.ejor.2009.12.027⟩. ⟨inria-00606436⟩



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