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Real-World Applications of Multiobjective Optimization

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Multiobjective Optimization

Abstract

This chapter presents a number of illustrative case studies of a wide range of applications of multiobjective optimization methods, in areas ranging from engineering design to medical treatments. The methods used include both conventional mathematical programming and evolutionary optimization, and in one case an integration of the two approaches. Although not a comprehensive review, the case studies provide evidence of the extent of the potential for using classical and modern multiobjective optimization in practice, and opens many opportunities for further research.

Reviewed by: Alexander Lotov, Russian Academy of Sciences, Russia; Tatsuya Okabe, Honda Research and Development Inc., Japan; Kalyanmoy Deb, Indian Institute of Technology Kanpur, India

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References

  • Aerts, J.C.J.H., van Herwijnen, M., Janssen, R., Stewart, T.J.: Evaluating spatial design techniques for solving land-use allocation problems. Journal of Environmental Planning and Management 48(1), 121–142 (2005)

    Article  Google Scholar 

  • Alexandrov, N.M., Dennis, J.E., Lewis, R.M., Torczon, V.: A trust region framework for managing use of approximation models in optimization. Journal on Structural Optimization 15(1), 16–23 (1998)

    Article  Google Scholar 

  • Arima, T., Sonoda, T., Shirotori, M., Tamura, A., Kikuchi, K.: A numerical investigation of transonic axial compressor rotor flow using a low-Reynolds-number k − ε turbulence model. ASME Journal of Turbomachinery 121(1), 44–58 (1999)

    Article  Google Scholar 

  • Bandte, O., Malinchik, S.: A broad and narrow approach to interactive evolutionary design – an aircraft design example. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 883–895. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  • Bartsch, H., Bickenbach, P.: Supply Chain Management mit SAP APO. Galileo Press, Bonn (2001)

    Google Scholar 

  • Belton, V., Stewart, T.J.: Multiple Criteria Decision Analysis: An Integrated Approach. Kluwer Academic Publishers, Boston (2002)

    Book  Google Scholar 

  • Benson, H.: An outer approximation algorithm for generating all efficient extreme points in the outcome set of a multiple objective linear programming problem. Journal of Global Optimization 13(1), 1–24 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  • Buonanno, M., Lim, C., Mavris, D.N.: Impact of configuration and requirements on the sonic boom of a quiet supersonic jet. Presented at World Aviation Congress, Phoenix, AZ (2002)

    Google Scholar 

  • Charnes, A., Cooper, W.: Management Models and Industrial Applications of Linear Programming, vol. 1. John Wiley, New York (1961)

    MATH  Google Scholar 

  • Dickersbach, J.T.: Supply Chain Management with APO, 2nd edn. Springer, Berlin (2005)

    Google Scholar 

  • Ehrgott, M., Winz, I.: Interactive decision support in radiation therapy treatment planning. OR Spectrum 30, 311–329 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  • Ehrgott, M., Holder, A., Reese, J.: Beam selection in radiotherapy design. In: Linear Algebra and Its Applications, vol. 428, pp. 1272–1312 (2008a)

    Google Scholar 

  • Ehrgott, M., Hamacher, H.W., Nußbaum, M.: Decomposition of matrices and static multileaf collimators: A survey. In: Alves, C.J.S., Pardalos, P.M., Vicente, L.N. (eds.) Optimization in Medicine. Springer Series in Optimization and Its Applications, vol. 12, pp. 25–46. Springer Science & Business Media, New York (2008b)

    Chapter  Google Scholar 

  • Emmerich, M., Giannakoglou, K., Naujoks, B.: Single and multi-objective evolutionary optimization assisted by Gaussian random field meta-models. IEEE Transactions on Evolutionary Computation 10(4), 421–439 (2006)

    Article  Google Scholar 

  • Fleischmann, B., Meyr, H., Wagner, M.: Advanced planning. In: Stadtler, H., Kilger, C. (eds.) Supply Chain Management and Advanced Planning. Concepts, Models, Software and Case Studies, 3rd edn., pp. 81–106. Springer, Berlin (2005)

    Chapter  Google Scholar 

  • Hasenjäger, M., Sendhoff, B., Sonoda, T., Arima, T.: Three dimensional evolutionary aerodynamic design optimization using single and multi-objective approaches. In: Schilling, R., Haase, W., Periaux, J., Baier, H., Bugeda, G. (eds.) Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems EUROGEN 2005, Munich, FLM (2005)

    Google Scholar 

  • Haupt, R.L., Haupt, S.E.: Practical Genetic Algorithms. John Wiley & Sons, New York (1998)

    MATH  Google Scholar 

  • Holder, A.: Designing radiotherapy plans with elastic constraints and interior point methods. Health Care Management Science 6, 5–16 (2003)

    Article  Google Scholar 

  • Holland, J.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  • Janssen, R., van Herwijnen, M., Stewart, T.J., Aerts, J.C.J.H.: Multiobjective decision support for land use planning. Environment and Planning B, Planning and Design. To appear (2007)

    Google Scholar 

  • Jin, Y.: A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing 9(1), 3–12 (2005)

    Article  Google Scholar 

  • Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments – A survey. IEEE Transactions on Evolutionary Computation 9(3), 303–317 (2005)

    Article  Google Scholar 

  • Jin, Y., Olhofer, M., Sendhoff, B.: On evolutionary optimization with approximate fitness functions. In: Genetic and Evolutionary Computation Conference, pp. 786–792. Morgan Kaufmann, San Francisco (2000)

    Google Scholar 

  • Jin, Y., Olhofer, M., Sendhoff, B.: A framework for evolutionary optimization with approximate fitness functions. IEEE Transactions on Evolutionary Computation 6(5), 481–494 (2002)

    Article  Google Scholar 

  • Jin, Y., Olhofer, M., Sendhoff, B.: On evolutionary optimization of large problems using small populations. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3611, pp. 1145–1154. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  • Jin, Y., Zhou, A., Zhang, Q., Tsang, E.: Modeling regularity to improve scalability of model-based multi-objective optimization algorithms. In: Multiobjective Problem Solving from Nature. Natural Computing Series, pp. 331–356. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  • Larranaga, P., Lozano, J.A. (eds.): Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Dordrecht (2001)

    Google Scholar 

  • Li, X.-D.: A real-coded predator-prey genetic algorithm for multiobjective optimization. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 207–221. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  • Lim, D., Ong, Y.-S., Jin, Y., Sendhoff, B., Lee, B.S.: Inverse multi-objective robust evolutionary optimization. Genetic Programming and Evolvable Machines 7(4), 383–404 (2007)

    Article  Google Scholar 

  • MacKerell Jr., A.D.: Empirical force fields for biological macromolecules: Overview and issues. Journal of Computational Chemistry 25(13), 1584–1604 (2004)

    Article  Google Scholar 

  • Mitchell, M.: An Introduction to Genetic Algorithms. The MIT Press, Cambridge (1996)

    MATH  Google Scholar 

  • Morris, G.M., Goodsell, D.S., Halliday, R.S., Huey, R., Hart, W.E., Belew, R.K., Olson, A.J.: Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of Computational Chemistry 19(14), 1639–1662 (1998)

    Article  Google Scholar 

  • Nakayama, H.: Aspiration level approach to interactive multi-objective programming and its applications. In: Pardalos, P.M., Siskos, Y., Zopounidis, C. (eds.) Advances in Multicriteria Analysis, pp. 147–174. Kluwer Academic Publishers, Dordrecht (1995)

    Chapter  Google Scholar 

  • Nakayama, H., Sawaragi, Y.: Satisficing trade-off method for interactive multiobjective programming methods. In: Grauer, M., Wierzbicki, A.P. (eds.) Interactive Decision Analysis – Proceedings of an International Workshop on Interactive Decision Analysis and Interpretative Computer Intelligence, pp. 113–122. Springer, Heidelberg (1984)

    Google Scholar 

  • Nakayama, H., Kaneshige, S., Takemoto, S., Watada, Y.: An application of a multi-objective programming technique to construction accuracy control of cable-stayed bridges. European Journal of Operational Research 87, 731–738 (1995)

    Article  MATH  Google Scholar 

  • Obayashi, S., Sasaki, D., Takaguchi, Y., Hirose, N.: Multi-objective evolutionary computation for supersonic wing-shape optimization. IEEE Transactions on Evolutionary Computation 4(2), 182–187 (2000)

    Article  Google Scholar 

  • Okabe, T., Foli, K., Olhofer, M., Jin, Y., Sendhoff, B.: Comparative Studies on Micro Heat Exchanger Optimisation. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC-2003), pp. 647–654. IEEE Computer Society Press, Los Alamitos (2003)

    Google Scholar 

  • Olhofer, M., Arima, T., Sonoda, T., Sendhoff, B.: Optimization of a stator blade used in a transonic compressor cascade with evolution strategies. In: Parmee, I. (ed.) Adaptive Computing in Design and Manufacture, pp. 45–54. Springer, Heidelberg (2000)

    Google Scholar 

  • Olhofer, M., Jin, Y., Sendhoff, B.: Adaptive encoding for aerodynamic shape optimization using evolution strategies. In: Congress on Evolutionary Computation (CEC), Seoul, Korea, May 2001, vol. 2, pp. 576–583. IEEE Computer Society Press, Los Alamitos (2001)

    Google Scholar 

  • Ong, Y.-S., Nair, P.B., Lim, K.Y.: Max-min surrogate-assisted evolutionary algorithms for robust design. IEEE Transactions on Evolutionary Computation 10(4), 392–404 (2006)

    Article  Google Scholar 

  • Paenke, I., Branke, J., Jin, Y.: Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation. IEEE Transactions on Evolutionary Computation 10, 405–420 (2006)

    Article  Google Scholar 

  • Pettersson, F., Chakraborti, N., Saxén, H.: A genetic algorithms based multiobjective neural net applied to noisy blast furnace data. Applied Soft Computing 7, 387–397 (2007a)

    Article  Google Scholar 

  • Pettersson, F., Chakraborti, N., Singh, S.B.: Neural networks analysis of steel plate processing augmented by multi-objective genetic algorithms. Steel Research International 78, 890–898 (2007b)

    Article  Google Scholar 

  • Poloni, C., Pediroda, V.: GA coupled with computationally expensive simulations: tools to improve efficiency. In: Genetic Algorithms and Evolution Strategies in Engineering and Computer Science, pp. 267–288. John Wiley and Sons, Chichester (1997)

    Google Scholar 

  • Price, K., Storn, R.N., Lampinen, J.A. (eds.): Differential Evolution: A Practical Approach to Global Optimizations. Springer, Berlin (2005)

    Google Scholar 

  • Saxén, H., Pettersson, F., Gunturu, K.: Evolving nonlinear time-series models of the hot metal silicon content in the blast furnace. Materials and Manufacturing Processes 22, 577–584 (2007)

    Article  Google Scholar 

  • Shao, L.: A survey of beam intensity optimization in IMRT. In: Halliburton, T. (ed.) Proceedings of the 40th Annual Conference of the Operational Research Society of New Zealand, Wellington, 2-3 December 2005, pp. 255–264 (2005), Available online at http://secure.orsnz.org.nz/conf40/content/paper/Shao.pdf

  • Shao, L., Ehrgott, M.: Finding representative nondominated points in multiobjective linear programming. In: IEEE Symposium on Computational Intelligence in Multi-Criteria Decision Making, pp. 245–252. IEEE Computer Society Press, Los Alamitos (2007)

    Chapter  Google Scholar 

  • Shao, L., Ehrgott, M.: Approximately solving multiobjective linear programmes in objective space and an application in radiotherapy treatment planning. Mathematical Methods of Operations Research (2008)

    Google Scholar 

  • Stewart, T.J., Janssen, R., van Herwijnen, M.: A genetic algorithm approach to multiobjective land use planning. Computers and Operations Research 32, 2293–2313 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  • Takagi, H.: Interactive evolutionary computation: Fusion of the capacities of EC optimization and human evaluation. Proceedings of the IEEE 89, 1275–1296 (2001)

    Article  Google Scholar 

  • The MathWorks Inc. (2008)

    Google Scholar 

  • Tsutsui, S., Ghosh, A.: Genetic algorithms with a robust solution searching scheme. IEEE Transactions on Evolutionary Computation 1(3), 201–208 (1997)

    Article  Google Scholar 

  • Wierzbicki, A.P.: Reference point approaches. In: Gal, T., Stewart, T.J., Hanne, T. (eds.) Multicriteria Decision Making: Advances in MCDM Models, Algorithms, Theory, and Applications, Kluwer Academic Publishers, Boston (1999)

    Google Scholar 

  • Zhang, Q., Zhou, A., Jin, Y.: RM-MEDA: A regularity model-based multi-objective estimation of distribution algorithm. IEEE Transactions on Evolutionary Computation 12(1), 41–63 (2008)

    Article  Google Scholar 

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Stewart, T. et al. (2008). Real-World Applications of 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_11

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  • DOI: https://doi.org/10.1007/978-3-540-88908-3_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-88907-6

  • Online ISBN: 978-3-540-88908-3

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