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Can Single-Objective Optimization Profit from Multiobjective Optimization?

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Multiobjective Problem Solving from Nature

Part of the book series: Natural Computing Series ((NCS))

Summary

Many real-world problems are multiobjective optimization problems, and evolutionary algorithms are quite successful on such problems. Since the task is to compute or approximate the Pareto front, multiobjective optimization problems are considered as more difficult than single-objective problems. One should not forget that the fitness vector with respect to more than one objective contains more information that in principle can direct the search of evolutionary algorithms. Therefore, it is possible that a single-objective problem can be solved more efficiently via a generalized multiobjective model of the problem. That this is indeed the case is proved by investigating the single-source shortest paths problem and the computation of minimum spanning trees.

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Author information

Authors and Affiliations

  1. Algorithms and Complexity, Max-Planck-Institut für Informatik, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany

    Frank Neumann

  2. FB Informatik, LS 2, Univ. Dortmund, 44221 Dortmund, Germany

    Ingo Wegener

Authors
  1. Frank Neumann
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  2. Ingo Wegener
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Editor information

Editors and Affiliations

  1. Manchester Interdisciplinary Biocentre, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK

    Joshua Knowles

  2. Room G39 Earl Mountbatten Building, Heriot-Watt University, Edinburgh EH14 4AS, UK

    David Corne

  3. Dept. of Business Technology, Helsinki School of Economics, P.O. Box 1210, FIN-00101 Helsinki, Finland

    Kalyanmoy Deb (Deva Raj Chair Professor) (Deva Raj Chair Professor)

  4. Dept. of Mechanical Engineering, Indian Institute of Technology Kanpur, Kanpur,Pin 208016, India

    Kalyanmoy Deb (Deva Raj Chair Professor) (Deva Raj Chair Professor)

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Neumann, F., Wegener, I. (2008). Can Single-Objective Optimization Profit from Multiobjective Optimization?. In: Knowles, J., Corne, D., Deb, K. (eds) Multiobjective Problem Solving from Nature. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72964-8_6

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  • DOI: https://doi.org/10.1007/978-3-540-72964-8_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72963-1

  • Online ISBN: 978-3-540-72964-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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