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Combining (Integer) Linear Programming Techniques and Metaheuristics for Combinatorial Optimization

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Hybrid Metaheuristics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 114))

Several different ways exist for approaching hard optimization problems. Mathematical programming techniques, including (integer) linear programming based methods, and metaheuristic approaches are two highly successful streams for combinatorial problems. These two have been established by different communities more or less in isolation from each other. Only over the last years a larger number of researchers recognized the advantages and huge potentials of building hybrids of mathematical programming methods and metaheuristics. In fact, many problems can be practically solved much better by exploiting synergies between these different approaches than by “pure” traditional algorithms. The crucial issue is howmathematical programming methods and metaheuristics should be combined for achieving those benefits. Many approaches have been proposed in the last few years. After giving a brief introduction to the basics of integer linear programming, this chapter surveys existing techniques for such combinations and classifies them into ten methodological categories.

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Authors and Affiliations

  1. Institute of Computer Graphics and Algorithms, Vienna University of Technology, Vienna, Austria

    Günther R. Raidl

  2. NICTA Victoria Laboratory, University of Melbourne, Melbourne, Australia

    Jakob Puchinger

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Editors and Affiliations

  1. Dept. Llenguatges i Sistemes Informátics, Universitat Politècnica de Catalunya, Jordi Girona 1-3 Omega 112, E-08034, Barcelona, Spain

    Christian Blum

  2. Dept. Llenguatges i Sistemes Informátics, Universitat Politècnica de Catalunya, Jordi Girona 1-3 Omega 213, E-08034, Barcelona, Spain

    Maria José Blesa Aguilera

  3. Università di Bologna, Via Venezia 52, I-47023, Cesena, Italy

    Andrea Roli

  4. Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, B-1050, Brussels, Belgium

    Michael Sampels

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Raidl, G.R., Puchinger, J. (2008). Combining (Integer) Linear Programming Techniques and Metaheuristics for Combinatorial Optimization. In: Blum, C., Aguilera, M.J.B., Roli, A., Sampels, M. (eds) Hybrid Metaheuristics. Studies in Computational Intelligence, vol 114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78295-7_2

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