Abstract
In this paper, we describe an interactive evolutionary algorithm called Interactive WASF-GA to solve multiobjective optimization problems. This algorithm is based on a preference-based evolutionary multiobjective optimization algorithm called WASF-GA. In Interactive WASF-GA, a decision maker provides preference information at each iteration simply as a reference point consisting of desirable objective function values and the number of solutions to be compared. Using this information, the desired number of solutions is generated to represent the region of interest of the Pareto optimal front associated to the reference point given. Interactive WASF-GA implies a much lower computational cost than the original WASF-GA because it generates a small number of solutions. This speeds up the convergence of the algorithm, making it suitable for many decision-making problems. Its efficiency and usefulness is demonstrated with a five-objective optimization problem.
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.
Similar content being viewed by others
References
Coello, C.A.C., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer, New York (2007)
Deb, K.: Multi-objective Optimization using Evolutionary Algorithms. Wiley, Chichester (2001)
Deb, K., Miettinen, K., Chaudhuri, S.: Towards an estimation of nadir objective vector using a hybrid of evolutionary and local search approaches. IEEE Transactions on Evolutionary Computation 14(6), 821–841 (2010)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Deb, K., Sundar, J., Ubay, B., Chaudhuri, S.: Reference point based multi-objective optimization using evolutionary algorithm. International Journal of Computational Intelligence Research 2(6), 273–286 (2006)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable multi-objective optimization test problems. In: Congress on Evolutionary Computation, CEC-2002, pp. 825–830 (2002)
Durillo, J.J., Nebro, A.J.: jMetal: A java framework for multi-objective optimization. Advances in Engineering Software 42, 760–771 (2011)
Figueira, J.R., Greco, S., Mousseau, V., Słowiński, R.: Interactive multiobjective optimization using a set of additive value functions. In: Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds.) Multiobjective Optimization. LNCS, vol. 5252, pp. 97–119. Springer, Heidelberg (2008)
Gong, M., Liu, F., Zhang, W., Jiao, L., Zhang, Q.: Interactive MOEA/D for multi-objective decision making. In: 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 721–728 (2011)
Huband, S., Hingston, P., Barone, L., While, L.: A review of multiobjective test problems and a scalable test problem toolkit. IEEE Transactions on Evolutionary Computation 10(5), 477–506 (2006)
Luque, M., Ruiz, F., Steuer, R.E.: Modified interactive Chebyshev algorithm (MICA) for convex multiobjective programming. European Journal of Operational Research 204(3), 557–564 (2010)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations. In: 5-th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Pressley, Berkeley (1967)
Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Boston (1999)
Miettinen, K.: Survey of methods to visualize alternatives in multiple criteria decision making problems. OR Spectrum 36(1), 3–37 (2014)
Miettinen, K., Mäkelä, M.M.: On scalarizing functions in multiobjective optimization. OR Spectrum 24(2), 193–213 (2002)
Miettinen, K., Ruiz, F., Wierzbicki, A.P.: Introduction to multiobjective optimization: interactive approaches. In: Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds.) Multiobjective Optimization. LNCS, vol. 5252, pp. 27–57. Springer, Heidelberg (2008)
Ruiz, A.B., Saborido, R., Luque, M.: A preference-based evolutionary algorithm for multiobjective optimization: The weighting achievement scalarizing function genetic algorithm. Journal of Global Optimization (2014, in press). doi:10.1007/s10898-014-0214-y
Sindhya, K., Ruiz, A.B., Miettinen, K.: A preference based interactive evolutionary algorithm for multi-objective optimization: PIE. In: Takahashi, R.H.C., Deb, K., Wanner, E.F., Greco, S. (eds.) EMO 2011. LNCS, vol. 6576, pp. 212–225. Springer, Heidelberg (2011)
Sinha, A., Korhonen, P., Wallenius, J., Deb, K.: An interactive evolutionary multi-objective optimization algorithm with a limited number of decision maker calls. European Journal of Operational Research 233(3), 674–688 (2014)
Szczepanski, M., Wierzbicki, A.P.: Application of multiple crieterion evolutionary algorithm to vector optimization, decision support and reference point approaches. Journal of Telecommunications and Information Technology 3(3), 16–33 (2003)
Thiele, L., Miettinen, K., Korhonen, P., Molina, J.: A preference-based evolutionary algorithm for multi-objective optimization. Evolutionary Computation 17(3), 411–436 (2009)
Wang, R., Purshouse, R.C., Fleming, P.J.: Preference-inspired coevolutionary algorithms for many-objective optimization. IEEE Transactions on Evolutionary Computation 17(4), 474–494 (2013)
Wang, R., Purshouse, R.C., Fleming, P.J.: “Whatever works best for you”- a new method for a priori and progressive multi-objective optimisation. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds.) EMO 2013. LNCS, vol. 7811, pp. 337–351. Springer, Heidelberg (2013)
Wierzbicki, A.P.: The use of reference objectives in multiobjective optimization. In: Fandel, G., Gal, T. (eds.) Multiple Criteria Decision Making, Theory and Applications, pp. 468–486. Springer (1980)
Zhang, Q., Li, H.: MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation 11(6), 712–731 (2007)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation 8(2), 173–195 (2000)
Zitzler, E., Künzli, S.: Indicator-based selection in multiobjective search. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN VIII. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ruiz, A.B., Luque, M., Miettinen, K., Saborido, R. (2015). An Interactive Evolutionary Multiobjective Optimization Method: Interactive WASF-GA. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C. (eds) Evolutionary Multi-Criterion Optimization. EMO 2015. Lecture Notes in Computer Science(), vol 9019. Springer, Cham. https://doi.org/10.1007/978-3-319-15892-1_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-15892-1_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15891-4
Online ISBN: 978-3-319-15892-1
eBook Packages: Computer ScienceComputer Science (R0)