Abstract
The concept of optimization refers to the process of finding one or more feasible solutions of a problem which corresponds to the extreme values (either maximum or minimum) of one or more objective functions. Initial approaches to optimization were focused on the case of solving problems involving only one objective. However, as most real-world optimization problems involve many objectives the research on this area has rapidly broaden this attention to encompass what has been called multi-objective optimization.
This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM MADRINET S-0505/TIC/0255 and DPS2008-07029-C02-02.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
Bäck, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, Inc., Oxford (1996)
Deb, K.: Evolutionary Algorithms for Multi-Criterion Optimization in Engineering Design. In: Evolutionary Algorithms in Engineering and Computer Science, ch. 8. John Wiley & Sons Ltd., Chichester (1999)
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
Shaffer, J.H.D.: Multiple objective optimization with vector evaluated genetic algorithms. In: Proc. of the First International Conference on Genetic Algorithms, pp. 93–100 (1985)
Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multibojective optimization: Formulation, discusión and generalization. In: Forrest, S. (ed.) Proceedings of the Fifth Int Conf on Genetic Algorithms, pp. 416–423. Morgan Kauffman, San Mateo (1993)
Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionay Computation 2(3), 221–248 (1994)
Horn, J., Nafpliotsis, N., Goldberg, D.E.: A niched pareto genetic algorithm for multiobjective optimization. In: Proc. of the First IEEE Conf on Evolutionary Computation. IEEE World Congress on Computational Computation, Piscataway, NJ, vol. I, pp. 82–87. IEEE Press, Los Alamitos (1994)
Corne, D.W., Knowles, J.D., Oates, M.J.: The pareto envelope-based selection algorithm for multiobjective optimisation. In: Deb, K., Rudolph, G., Lutton, E., Merelo, J.J., Schoenauer, M., Schwefel, H.-P., Yao, X. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 839–848. Springer, Heidelberg (2000)
Deb, K., Pratap, A., Agrawal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on Evolutionary Computation 6(2), 182–197
Zitzler, E., Thiele, L.: An evolutionary algorithm for multiobjective optimization: The strength pareto approach. Technical report, Computer Engineering and Communication Networks Lab (TIK), Swiss Federal Institute of Technology, ETH (1998)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH) Zurich, Switzerland (May 2001)
Knowles, J.D., Corne, D.W.: The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Pareto Multiobjective Optimisation. In: Proceedings of the 1999 Congress on Evolutionary Computation (CEC 1999), vol. 1, pp. 98–105 (1999)
Corne, D.W., Jerram, N.R., Knowles, J.D., Oates, M.J.: PESA-II: Region-based Selection in Evolutionary Multiobjective Optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 283–290. Morgan Kaufmann Publishers, San Francisco (2001)
Becerra, R.L., Coello, C.A.: A Cultural Algorithm for Solving the Job-Shop Scheduling Problem. In: Jin, Y. (ed.) Knowledge Incorporation in Evolutionary Computation. Studies in Fuzziness and Soft Computing, vol. 167, pp. 37–55. Springer, Heidelberg
Tan, K.C., Khor, E.F., Lee, T.H.: Multiobjective Evolutionary Algorithms and Applications. Series: Advanced Information and Knowledge Processing. Springer, United Kingdom (2005)
Deb, K.: Evolutionary Algorithms for Multi-Criterion Optimization in Engineering Design. In: Evolutionary Algorithms in Engineering and Computer Science, ch. 8. John Wiley & Sons Ltd., Chichester (1999)
Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer Publishers, Boston (1999)
Herrero, J.G., Berlanga, A., Lopez, J.M.M.: Effective Evolutionary Algorithms for Many-Specifications Attainment: Application to Air Traffic Control Tracking Filters. IEEE Transactions on Evolutionary Computation 13(1), 151–168 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Berlanga, A., Herrero, J.G., Molina, J.M. (2009). Multiobjective Evolutionary Algorithms: Applications in Real Problems. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_89
Download citation
DOI: https://doi.org/10.1007/978-3-642-02478-8_89
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02477-1
Online ISBN: 978-3-642-02478-8
eBook Packages: Computer ScienceComputer Science (R0)