Abstract
Over the years, many interactive multiobjective optimization methods based on a reference point have been proposed. With a reference point, the decision maker indicates desirable objective function values to iteratively direct the solution process. However, when analyzing the performance of these methods, a critical issue is how to systematically involve decision makers. A recent approach to this problem is to replace a decision maker with an artificial one to be able to systematically evaluate and compare reference point based interactive methods in controlled experiments. In this study, a new artificial decision maker is proposed, which reuses the dynamics of particle swarm optimization for guiding the generation of consecutive reference points, hence, replacing the decision maker in preference articulation. We use the artificial decision maker to compare interactive methods. We demonstrate the artificial decision maker using the DTLZ benchmark problems with 3, 5 and 7 objectives to compare R-NSGA-II and WASF-GA as interactive methods. The experimental results show that the proposed artificial decision maker is useful and efficient. It offers an intuitive and flexible mechanism to capture the current context when testing interactive methods for decision making.
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Notes
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Without loss of generality, we use minimization in definitions.
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References
Miettinen, K.: Nonlinear Multiobjective Optimization. Kluwer, Boston (1999)
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). https://doi.org/10.1007/978-3-540-88908-3_2
Miettinen, K., Hakanen, J., Podkopaev, D.: Interactive nonlinear multiobjective optimization methods. In: Greco, S., Ehrgott, M., Figueira, J. (eds.) Multiple Criteria Decision Analysis. ISOR, vol. 233, pp. 931–980. Springer, New York (2016). https://doi.org/10.1007/978-1-4939-3094-4_22
López-Ibáñez, M., Knowles, J.: Machine decision makers as a laboratory for interactive EMO. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C.C. (eds.) EMO 2015. LNCS, vol. 9019, pp. 295–309. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15892-1_20
Purshouse, R.C., Deb, K., Mansor, M.M., Mostaghim, S., Wang, R.: A review of hybrid evolutionary multiple criteria decision making methods. In: Proceedings of IEEE Congress on Evolutionary Computation, pp. 1147–1154 (2014)
Steuer, R.E.: Multiple Criteria Optimization: Theory, Computation, and Applications. Wiley, Hoboken (1986)
Ojalehto, V., Podkopaev, D., Miettinen, K.: Towards automatic testing of reference point based interactive methods. In: Handl, J., Hart, E., Lewis, P.R., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds.) PPSN 2016. LNCS, vol. 9921, pp. 483–492. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45823-6_45
Deb, K., Sundar, J.: Reference point based multi-objective optimization using evolutionary algorithms. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, ACM pp. 635–642 (2006)
Ruiz, A.B., Luque, M., Miettinen, K., Saborido, R.: An interactive evolutionary multiobjective optimization method: interactive WASF-GA. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C.C. (eds.) EMO 2015. LNCS, vol. 9019, pp. 249–263. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-15892-1_17
PSO-Central-Group: Standard PSO 2006, 2007, and 2011. Technical report, Particle Swarm Central, January 2011. http://www.particleswarm.info/
Durillo, J.J., Nebro, A.J.: jMetal: a java framework for multi-objective optimization. Adv. Eng. Softw. 42(10), 760–771 (2011)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable multi-objective optimization test problems. In: Proceedings of the 2002 Congress on Evolutionary Computation. vol. 1, pp. 825–830. IEEE (2002)
Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. Chapman & Hall/CRC, California (2007)
Acknowledgements
This work was partially funded by Grants TIN2017-86049-R (Spanish MICINN) and P12-TIC-1519 (PAIDI). C. Barba-González was supported by Grant BES-2015-072209 (Spanish MICINN) and University of Jyväskylä. J. García-Nieto is the recipient Post-Doct fellowship of “Plan Propio” at Universidad de Málaga. This work was supported on the part of V. Ojalehto by the Academy of Finland (grant number 287496).
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Barba-González, C., Ojalehto, V., García-Nieto, J., Nebro, A.J., Miettinen, K., Aldana-Montes, J.F. (2018). Artificial Decision Maker Driven by PSO: An Approach for Testing Reference Point Based Interactive Methods. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11101. Springer, Cham. https://doi.org/10.1007/978-3-319-99253-2_22
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