Approximating the Nondominated Frontiers of Multi-Objective Combinatorial Optimization Problems

Date

2009-03-01

Author

Koksalan, Murat
Lokman, Banu

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Finding, all nondominated vectors for multi-objective combinatorial optimization (MOCO) problems is computationally very hard in general. We approximate the nondominated Frontiers of MOCO problems by fitting smooth hypersurfaces. For a given problem, we lit the hypersurface using a single nondominated reference vector. We experiment with different types of MOCO problems and demonstrate that in all cases the fitted hypersurfaces approximate all nondominated vectors well. We discuss that such an approximation is useful to find the neighborhood of preferred regions of the nondominated vectors with very little computational effort. Further computational effort can then be spent in the identified region to find the actual nondominated vectors the decision maker will prefer. (C) 2009 Wiley Periodical, Inc. Naval Research Logistics 56: 191-198, 2009

Subject Keywords

Multi-objective combinatorial optimization, Nondominated vectors, Surface fitting

URI

https://hdl.handle.net/11511/32655

Journal

NAVAL RESEARCH LOGISTICS

DOI

https://doi.org/10.1002/nav.20336

Collections

Graduate School of Natural and Applied Sciences, Article

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Citation Formats

M. Koksalan and B. Lokman, “Approximating the Nondominated Frontiers of Multi-Objective Combinatorial Optimization Problems,” NAVAL RESEARCH LOGISTICS, pp. 191–198, 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32655.