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A Comparative Study of Progressive Preference Articulation Techniques for Multiobjective Optimisation

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4403))

Abstract

Multiobjective optimisation has traditionally focused on problems consisting of 2 or 3 objectives. Real-world problems often require the optimisation of a larger number of objectives. Research has shown that conclusions drawn from experimentations carried out on 2 or 3 objectives cannot be generalized for a higher number of objectives. The curse of dimensionality is a problem that faces decision makers when confronted with many objectives. Preference articulation techniques, and especially progressive preference articulation (PPA) techniques are effective methods for supporting the decision maker. In this paper, some of the most recent and most established PPA techniques are examined, and their utility for tackling many-objective optimisation problems is discussed and compared from the viewpoint of the decision maker.

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References

  1. Purshouse, R.C.: On the Evolutionary Optimisation of Many Objectives. Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield, UK (2003)

    Google Scholar 

  2. Khare, V., Yao, X., Deb, K.: Performance Scaling of Multi-Objective Evolutionary Algorithms. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  3. Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer, Boston (1999)

    MATH  Google Scholar 

  4. Deb, K., Saxena, D.K.: On Finding Pareto-Optimal Solutions Through Dimensionality Reduction for Certain Large-Dimensional Multi-Objective Optimization Problems. Kanpur Genetic Algorithms Laboratory (KanGAL) Indian Institute of Technology Kanpur, Kanpur (2005)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  6. Branke, J., Deb, K.: Integrating User Preferences into Evolutionary Multi-Objective Optimization. In: Jin, Y. (ed.) Knowledge Incorporation in Evolutionary Computation, pp. 461–477. Springer, Heidelberg (2005)

    Google Scholar 

  7. Deb, K., Mohan, M., Mishra, S.: Evaluating the ε-Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions. Evolutionary Computation 13(4), 501–525 (2005)

    Article  Google Scholar 

  8. 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, Part A: Systems and Humans 28(1), 26–37 (1998)

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  10. Deb, K.: Multi-objective Evolutionary Algorithms: Introducing Bias Among Pareto-optimal Solutions, in Advances in Evolutionary Computing. In: Ghosh, A., Tsutsui, S. (eds.) Theory and Applications, pp. 263–292. Springer, Berlin (2003)

    Google Scholar 

  11. Srinivas, N., Deb, K.: Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation 2(3), 221–248 (1994)

    Article  Google Scholar 

  12. Deb, K., et al.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) Parallel Problem Solving from Nature-PPSN VI. LNCS, vol. 1917, Springer, Heidelberg (2000)

    Google Scholar 

  13. Laumanns, M., et al.: Combining Convergence and Diversity in Evolutionary Multi-Objective Optimization. Evolutionary Computation 10(3), 263–282 (2002)

    Article  Google Scholar 

  14. Knowles, J.D., Corne, D.W.: Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation 8(2), 149–172 (2000)

    Article  Google Scholar 

  15. Inselberg, A.: The Plane with Parallel Coordinates. The Visual Computer 1, 69–91 (1985)

    Article  MATH  Google Scholar 

  16. Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2), 173–195 (2000)

    Article  Google Scholar 

  17. Fonseca, C.M., Fleming, P.J.: Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. In: Proc. of the Fifth Int. Conf. on Genetic Algorithms, Morgan Kaufmann, San Mateo (1993)

    Google Scholar 

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Shigeru Obayashi Kalyanmoy Deb Carlo Poloni Tomoyuki Hiroyasu Tadahiko Murata

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© 2007 Springer Berlin Heidelberg

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Adra, S.F., Griffin, I., Fleming, P.J. (2007). A Comparative Study of Progressive Preference Articulation Techniques for Multiobjective Optimisation. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds) Evolutionary Multi-Criterion Optimization. EMO 2007. Lecture Notes in Computer Science, vol 4403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70928-2_67

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  • DOI: https://doi.org/10.1007/978-3-540-70928-2_67

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70927-5

  • Online ISBN: 978-3-540-70928-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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