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International Conference on Parallel Problem Solving from Nature

PPSN 2008: Parallel Problem Solving from Nature – PPSN X pp 847–858Cite as

SPAM: Set Preference Algorithm for Multiobjective Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5199))

Abstract

This paper pursues the idea of a general multiobjective optimizer that can be flexibly adapted to arbitrary user preferences—assuming that the goal is to approximate the Pareto-optimal set. It proposes the Set Preference Algorithm for Multiobjective Optimization (SPAM) the working principle of which is based on two observations: (i) current multiobjective evolutionary algorithms (MOEAs) can be regarded as hill climbers on set problems and (ii) specific user preferences are often (implicitly) expressed in terms of a binary relation on Pareto set approximations. SPAM realizes a (1 + 1)-strategy on the space of Pareto set approximations and can be used with any type of set preference relations, i.e., binary relations that define a total preorder on Pareto set approximations. The experimental results demonstrate for a range of set preference relations that SPAM provides full flexibility with respect to user preferences and is effective in optimizing according to the specified preferences. It thereby offers a new perspective on preference-guided multiobjective search.

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References

  1. Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

  2. Zitzler, E., Künzli, S.: Indicator-Based Selection in Multiobjective Search. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN VIII 2004. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  3. Fonseca, C.M., Fleming, P.J.: Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms—Part I: A Unified Formulation. IEEE Transactions on Systems, Man, and Cybernetics 28(1), 26–37 (1998)

    Article  Google Scholar 

  4. Branke, J., Kaußler, T., Schmeck, H.: Guidance in Evolutionary Multi-Objective Optimization. Advances in Engineering Software 32, 499–507 (2001)

    Article  MATH  Google Scholar 

  5. Cvetković, D., Parmee, I.C.: Preferences and their Application in Evolutionary Multiobjective Optimisation. IEEE Transactions on Evolutionary Computation 6(1), 42–57 (2002)

    Article  Google Scholar 

  6. Branke, J., Deb, K.: Integrating User Preferences into Evolutionary Multi-Objective Optimization. Technical Report 2004004, Indian Institute of Technology, Kanpur, India (2004); In: Jin, Y. (ed). Knowledge Incorporation in Evolutionary Computation, pp. 461–477. Springer, Heidelberg (2004)

    Google Scholar 

  7. Deb, K., Sundar, J.: Reference Point Based Multi-Objective Optimization Using Evolutionary Algorithms. In: Keijzer, M., et al. (eds.) Conference on Genetic and Evolutionary Computation (GECCO 2006), pp. 635–642. ACM Press, New York (2006)

    Google Scholar 

  8. Rachmawati, L., Srinivasan, D.: Preference Incorporation in Multi-objective Evolutionary Algorithms: A Survey. In: IEEE Congress on Evolutionary Computation (CEC 2006), Vancouver, BC, Canada, pp. 3385–3391. IEEE Press, Los Alamitos (2006)

    Google Scholar 

  9. Mehnen, J., Trautmann, H., Tiwari, A.: Introducing User Preference Using Desirability Functions in Multi-Objective Evolutionary Optimisation of Noisy Processes. In: IEEE Congress on Evolutionary Computation (CEC 2007), pp. 2687–2694. IEEE Press, Los Alamitos (2007)

    Chapter  Google Scholar 

  10. Emmerich, M., Beume, N., Naujoks, B.: An EMO Algorithm Using the Hypervolume Measure as Selection Criterion. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 62–76. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  11. Igel, C., Hansen, N., Roth, S.: Covariance Matrix Adaptation for Multi-objective Optimization. Evolutionary Computation 15(1), 1–28 (2007)

    Article  Google Scholar 

  12. Zitzler, E., Brockhoff, D., Thiele, L.: The Hypervolume Indicator Revisited: On the Design of Pareto-compliant Indicators Via Weighted Integration. In: Obayashi, S., et al. (eds.) EMO 2007. LNCS, vol. 4403, pp. 862–876. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  13. Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)

    Article  Google Scholar 

  14. Knowles, J., Corne, D.: On Metrics for Comparing Non-Dominated Sets. In: Congress on Evolutionary Computation (CEC 2002), pp. 711–716. IEEE Computer Society Press, Piscataway (2002)

    Google Scholar 

  15. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., Grunert da Fonseca, V.: Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation 7(2), 117–132 (2003)

    Article  Google Scholar 

  16. Zitzler, E., Thiele, L., Bader, J.: On Set-Based Multiobjective Optimization. Technical Report 300, Computer Engineering and Networks Laboratory, ETH Zurich (2008)

    Google Scholar 

  17. Hansen, M.P., Jaszkiewicz, A.: Evaluating the quality of approximations of the non-dominated set. Technical report, Institute of Mathematical Modeling, Technical University of Denmark, IMM Technical Report IMM-REP-1998-7 (1998)

    Google Scholar 

  18. Veldhuizen, D.A.V., Lamont, G.B.: Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art. Evolutionary Computation 8(2), 125–147 (2000)

    Article  Google Scholar 

  19. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization. In: Giannakoglou, K., et al. (eds.) Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001), International Center for Numerical Methods in Engineering (CIMNE), pp. 95–100 (2002)

    Google Scholar 

  20. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  21. Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 2(3), 221–248 (1994)

    Article  Google Scholar 

  22. Brockhoff, D., Friedrich, T., Hebbinghaus, N., Klein, C., Neumann, F., Zitzler, E.: Do Additional Objectives Make a Problem Harder? In: Thierens, D., et al. (eds.) Genetic and Evolutionary Computation Conference (GECCO 2007), pp. 765–772. ACM Press, New York (2007)

    Google Scholar 

  23. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. In: Schoenauer, M., et al. (eds.) PPSN VI 2000. LNCS, vol. 1917, pp. 849–858. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  24. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable Multi-Objective Optimization Test Problems. In: Congress on Evolutionary Computation (CEC 2002), pp. 825–830. IEEE Press, Los Alamitos (2002)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Engineering and Networks Laboratory, ETH Zurich, Switzerland

    Eckart Zitzler, Lothar Thiele & Johannes Bader

Authors
  1. Eckart Zitzler
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  2. Lothar Thiele
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  3. Johannes Bader
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Editor information

Editors and Affiliations

  1. Fakultät für Informatik, Technische Universität Dortmund, 44221, Dortmund, Germany

    Günter Rudolph

  2. Fakultät für Informatik, Technische Universität Dortmund, 44221, Dortmund, Germany

    Thomas Jansen  & Nicola Beume  & 

  3. Department of Computing and Electronic Systems, University of Essex, CO4 3SQ, Colchester, Essex, UK

    Simon Lucas

  4. Dipartimento di Ingegneria Meccanica, Università degli Studi di Trieste, 34127, Trieste, Italy

    Carlo Poloni

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© 2008 Springer-Verlag Berlin Heidelberg

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Zitzler, E., Thiele, L., Bader, J. (2008). SPAM: Set Preference Algorithm for Multiobjective Optimization. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87700-4_84

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  • DOI: https://doi.org/10.1007/978-3-540-87700-4_84

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87699-1

  • Online ISBN: 978-3-540-87700-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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