Abstract
Since their inception, multi-objective evolutionary algorithms have been adequately applied in finding a diverse approximation of efficient fronts of multi-objective optimization problems. In contrast, if we look at the rich history of classical multi-objective algorithms, we find that incorporation of user preferences has always been a major thrust of research. In this paper, we provide a general structure for incorporating preference information using multi-objective evolutionary algorithms. This is done in an NSGA-II scheme and by considering trade-off based preferences that come from so called proper Pareto-optimal solutions. We argue that finding proper Pareto-optimal solutions requires a set to compare with and hence, population based approaches should be a natural choice. Moreover, we suggest some practical modifications to the classical notion of proper Pareto-optimality. Computational studies on a number of test problems of varying complexity demonstrate the efficiency of multi-objective evolutionary algorithms in finding the complete preferred region for a large class of complex problems. We also discuss a theoretical justification for our NSGA-II based framework.
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References
Deb, K.: Innovization: Extracting innovative solution principles through multiobjective optimization. Springer, Heidelberg (2010)
Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer, Boston (1999)
Geoffrion, A.M.: Proper efficiency and the theory of vector maximization. Journal of Mathematical Analysis and Applications 22, 618–630 (1968)
Shukla, P.K.: In search of proper pareto-optimal solutions using multi-objective evolutionary algorithms. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4490, pp. 1013–1020. Springer, Heidelberg (2007)
Branke, J., Kauβler, T., Schmeck, H.: Guidance in evolutionary multi-objective optimization. Advances in Engineering Software 32, 499–507 (2001)
Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley, Chichester (2001)
Huang, V.L., Qin, A.K., Deb, K., Zitzler, E., Suganthan, P.N., Liang, J.J., Preuss, M., Huband, S.: Problem definitions for performance assessment of multi-objective optimization algorithms. Technical report, NTU, Singapore (2007)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multi-objective optimization. In: Abraham, A., et al. (eds.) Evolutionary Multiobjective Optimization, pp. 105–145. Springer, London (2005)
Huband, S., Hingston, P., Barone, L., White, L.: A review of multi-objective test problems and a scalable test problem toolkit. IEEE Transactions on Evolutionary Computation 10, 280–294 (2005)
Fonesca, C.M., Fleming, P.J.: On the performance assessment and comparison of stochastic multiobjective optimizers. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 584–593. Springer, Heidelberg (1996)
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Shukla, P.K., Hirsch, C., Schmeck, H. (2010). A Framework for Incorporating Trade-Off Information Using Multi-Objective Evolutionary Algorithms. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_14
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DOI: https://doi.org/10.1007/978-3-642-15871-1_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15870-4
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