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Multiobjective Optimization pp 157–178Cite as

Consideration of Partial User Preferences in Evolutionary Multiobjective Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5252))

Abstract

Evolutionary multiobjective optimization usually attempts to find a good approximation to the complete Pareto optimal front. However, often the user has at least a vague idea about what kind of solutions might be preferred. If such information is available, it can be used to focus the search, yielding a more fine-grained approximation of the most relevant (from a user’s perspective) areas of the Pareto optimal front and/or reducing computation time. This chapter surveys the literature on incorporating partial user preference information in evolutionary multiobjective optimization.

Reviewed by: Carlos Coello Coello, CINEVESTAV-IPN, Mexico; Salvatore Greco, University of Catania, Italy; Kalyanmoy Deb, Indian Institute of Technology Kanpur, India

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References

  • Branke, J., Deb, K.: Integrating user preference into evolutionary multi-objective optimization. In: Jin, Y. (ed.) Knowledge Incorporation in Evolutionary Computation, pp. 461–478. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  • Branke, J., Kaußler, T., Schmeck, H.: Guidance in evolutionary multi-objective optimization. Advances in Engineering Software 32, 499–507 (2001)

    Article  MATH  Google Scholar 

  • Branke, J., Deb, K., Dierolf, H., Osswald, M.: Finding knees in multi-objective optimization. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 722–731. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  • Brans, J.-P., Mareschal, B.: PROMETHEE methods. In: Figueira, J., et al. (eds.) Multiple criteria decision analysis, pp. 163–196. Springer, Heidelberg (2005)

    Google Scholar 

  • Coelho, R.F., Bersini, H., Bouillard, P.: Parametrical mechanical design with constraints and preferences: Application to a purge valve. Computer Methods in Applied Mechanics and Engineering 192, 4355–4378 (2003)

    Article  MATH  Google Scholar 

  • Coello Coello, C.A.: Handling preferences in evolutionary multiobjective optimization: A survey. In: Congress on Evolutionary Computation, vol. 1, pp. 30–37. IEEE Computer Society Press, Los Alamitos (2000)

    Google Scholar 

  • Coello Coello, C.A.: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: A survey of the state of the art. Computer Methods in Applied Mechanics and Engineering 191(11-12), 1245–1287 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  • Coello Coello, C.A., van Veldhuizen, D.A., Lamont, G.B.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, Dordrecht (2002)

    Book  MATH  Google Scholar 

  • Cvetkovic, 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 

  • Das, I.: On characterizing the ’knee’ of the pareto curve based on normal-boundary intersection. Structural Optimization 18(2/3), 107–115 (1999)

    Article  Google Scholar 

  • Deb, K.: Solving goal programming problems using multi-objective genetic algorithms. In: Proceedings of Congress on Evolutionary Computation, pp. 77–84 (1999)

    Google Scholar 

  • Deb, K.: An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering 186(2-4), 311–338 (2000)

    Article  MATH  Google Scholar 

  • Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

  • Deb, K.: Multi-objective evolutionary algorithms: Introducing bias among Pareto-optimal solutions. In: Ghosh, A., Tsutsui, S. (eds.) Advances in Evolutionary Computing: Theory and Applications, pp. 263–292. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  • Deb, K., Kumar, A.: Interactive evolutionary multi-objective optimization and decision-making using reference direction method. In: Genetic and Evolutionary Computation Conference, pp. 781–788. ACM Press, New York (2007a)

    Google Scholar 

  • Deb, K., Kumar, A.: Light beam search based multi-objective optimization using evolutionary algorithms. In: Congress on Evolutionary Computation, pp. 2125–2132. IEEE Computer Society Press, Los Alamitos (2007b)

    Google Scholar 

  • Deb, K., Sundar, J.: Reference point based multi-objective optimization using evolutionary algorithms. In: Genetic and Evolutionary Computation Conference, pp. 635–642. ACM Press, New York (2006)

    Google Scholar 

  • Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002a)

    Article  Google Scholar 

  • Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable multi-objective optimization test problems. In: Congress on Evolutionary Computation (CEC), pp. 825–830 (2002b)

    Google Scholar 

  • Deb, K., Sundar, J., Reddy, U.B., Chaudhuri, S.: Reference point based multi-objective optimization using evolutionary algorithms. International Journal of Computational Intelligence Research 2(3), 273–286 (2006)

    Article  MathSciNet  Google Scholar 

  • Emmerich, M.T.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 

  • Figueira, J., Mousseau, V., Roy, B.: ELECTRE methods. In: Figueia, J., Greco, S., Ehrgott, M. (eds.) Multiple Criteria Decision Analysis: State of the Art Surveys, pp. 134–162. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  • Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: Formulation, discussion, and generalization. In: International Conference on Genetic Algorithms, pp. 416–423 (1993)

    Google Scholar 

  • Fonseca, C.M., Fleming, P.J.: Multiobjective optimization and multiple constraint handling with evolutionary algorithms - part I: A unified fomulation. IEEE Transactions on Systems, Man, and Cybernetics - Part A 28(1), 26–37 (1998)

    Article  Google Scholar 

  • Greenwood, G.W., Hu, X.S., D’Ambrosio, J.G.: Fitness functions for multiple objective optimization problems: combining preferences with Pareto rankings. In: Belew, R.K., Vose, M.D. (eds.) Foundations of Genetic Algorithms, pp. 437–455. Morgan Kaufmann, San Francisco (1997)

    Google Scholar 

  • Horn, J.: Multicriterion Decision making. In: Bäck, T., Fogel, D., Michalewicz, Z. (eds.) Handbook of Evolutionary Computation, vol. 1, pp. F1.9:1–F1.9:15. Oxford University Press, Oxford (1997)

    Google Scholar 

  • Hughes, E.J.: Constraint handling with uncertain and noisy multi-objective evolution. In: Congress on Evolutionary Computation, pp. 963–970. IEEE Computer Society Press, Los Alamitos (2001)

    Google Scholar 

  • Jaszkiewicz, A., Slowinski, R.: The light beam search over a non-dominated surface of a multiple-objective programming problem. European Journal of Operational Research 113(2), 300–314 (1999)

    Article  MATH  Google Scholar 

  • Jiménez, F., Verdegay, J.L.: Evolutionary techniques for constrained optimization problems. In: Zimmermann, H.-J. (ed.) European Congress on Intelligent Techniques and Soft Computing, Verlag Mainz, Aachen (1999)

    Google Scholar 

  • Jin, Y., Sendhoff, B.: Incorporation of fuzzy preferences into evolutionary multiobjective optimization. In: Asia-Pacific Conference on Simulated Evolution and Learning, Nanyang Technical University, Singapore, pp. 26–30 (2002)

    Google Scholar 

  • Korhonen, P., Laakso, J.: A visual interactive method for solving the multiple criteria problem. European Journal of Operational Research 24, 277–287 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  • Molina, J., Santana, L.V., Hernandez-Diaz, A.G., Coello Coello, C.A., Caballero, R.: g-dominance: Reference point based dominance. European Journal of Operational Research (2009)

    Google Scholar 

  • Parreiras, R.O., Vasconcelos, J.A.: Decision making in multiobjective optimization problems. In: Nedjah, N., de Macedo Mourelle, L. (eds.) Real-World Multi-Objective System Engineering, pp. 29–52. Nova Science Publishers, New York (2005)

    Google Scholar 

  • Rachmawati, L., Srinivasan, D.: Preference incorporation in multi-objective evolutionary algorithms: A survey. In: Congress on Evolutionary Computation, pp. 3385–3391. IEEE Computer Society Press, Los Alamitos (2006)

    Google Scholar 

  • Rekiek, B., Lit, P.D., Fabrice, P., L’Eglise, T., Emanuel, F., Delchambre, A.: Dealing with users’s preferences in hybrid assembly lines design. In: Binder, Z., et al. (eds.) Management and Control of Production and Logistics Conference, pp. 989–994. Pergamon Press, Oxford (2000)

    Google Scholar 

  • Sait, S.M., Youssef, H., Ali, H.: Fuzzy simulated evolution algorithm for multi-objective optimization of VLSI placement. In: Congress on Evolutionary Computation, pp. 91–97. IEEE Computer Society Press, Los Alamitos (1999)

    Google Scholar 

  • Sakawa, M., Yauchi, K.: An interactive fuzzy satisficing method for multiobjective nonconvex programming problems through floating point genetic algorithms. European Journal of Operational Research 117, 113–124 (1999)

    Article  MATH  Google Scholar 

  • Schmiedle, F., Drechsler, N., Große, D., Drechsler, R.: Priorities in multi-objective optimization for genetic programming. In: Spector, L., et al. (eds.) Genetic and Evolutionary Computation Conference, pp. 129–136. Morgan Kaufmann, San Francisco (2002)

    Google Scholar 

  • Tan, K.C., Lee, T.H., Khor, E.F.: Evolutionary algorithms with goal and priority information for multi-objective optimization. In: Congress on Evolutionary Computation, pp. 106–113. IEEE Computer Society Press, Los Alamitos (1999)

    Google Scholar 

  • Thiele, L., Miettinen, K., Korhonen, P.J., Molina, J.: A preference-based interactive evolutionary algorithm for multiobjective optimization. Technical Report W-412, Helsinki School of Economics, Helsinki, Finland (2007)

    Google Scholar 

  • Trautmann, H., Mehnen, J.: A method for including a-priori-preference in multicriteria optimization. Technical Report 49/2005, SFG 475, University of Dortmund, Germany (2005)

    Google Scholar 

  • Van Veldhuizen, D., Lamont, G.B.: Multiobjective evolutionary algorithms: Analyzing the state-of-the-art. Evolutionary Computation Journal 8(2), 125–148 (2000)

    Article  Google Scholar 

  • White, C., Sage, A., Dozono, S.: A model of multiattribute decision-making and tradeoff weight determination under uncertainty. IEEE Transactions on Systems, Man, and Cybernetics 14, 223–229 (1984)

    Article  MathSciNet  Google Scholar 

  • Wierzbicki, A.P.: Basic properties of scalarizing functions for multiobjective optimization. Optimization 8(1), 55–60 (1977)

    MathSciNet  Google Scholar 

  • Wierzbicki, A.P.: On the completeness and constructiveness of parametric characterizations to vector optimization problems. OR Spektrum 8(2), 73–87 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  • Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

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

    Google Scholar 

  • Zitzler, E., Brockhoff, D., Thiele, L.: The hypervolume indicator revisited: On the design of pareto-compliant indicators via weighted integration. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 862–876. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

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  1. Jürgen Branke
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Editors and Affiliations

  1. Institute AIFB, University of Karlsruhe, Germany

    Jürgen Branke

  2. Department of Business Technology, Helsinki School of Economics, P.O. Box 1210, FI-00101, Helsinki, Finland

    Kalyanmoy Deb

  3. Department of Mathematical Information Technology, University of Jyväskylä, P.O. Box 35, (Agora), FI-40014, Finland

    Kaisa Miettinen

  4. Institute of Computing Science Poznan, University of Technology, 60-965, Poznan, Poland

    Roman Słowiński

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Branke, J. (2008). Consideration of Partial User Preferences in Evolutionary Multiobjective Optimization . In: Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds) Multiobjective Optimization. Lecture Notes in Computer Science, vol 5252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88908-3_6

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  • DOI: https://doi.org/10.1007/978-3-540-88908-3_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88907-6

  • Online ISBN: 978-3-540-88908-3

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