Abstract
In Simulation-based Evolutionary Multi-objective Optimization, the number of simulation runs is very limited, since the complex simulation models require long execution times. With the help of preference information, the optimization result can be improved by guiding the optimization towards relevant areas in the objective space with, for example, the Reference Point-based NSGA-II algorithm (R-NSGA-II) [4]. Since the Pareto-relation is the primary fitness function in R-NSGA-II, the algorithm focuses on exploring the objective space with high diversity. Only after the population has converged close to the Pareto-front does the influence of the reference point distance as secondary fitness criterion increase and the algorithm converges towards the preferred area on the Pareto-front.
In this paper, we propose a set of extensions of R-NSGA-II which adaptively control the algorithm behavior, in order to converge faster towards the reference point. The adaption can be based on criteria such as elapsed optimization time or the reference point distance, or a combination thereof. In order to evaluate the performance of the adaptive extensions of R-NSGA-II, a performance metric for reference point-based EMO algorithms is used, which is based on the Hypervolume measure called the Focused Hypervolume metric [12]. It measures convergence and diversity of the population in the preferred area around the reference point. The results are evaluated on two benchmark problems of different complexity and a simplistic production line model.
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Brockhoff, D., Bader, J., Thiele, L., Zitzler, E.: Directed multiobjective optimization based on the weighted hypervolume indicator. J. Multi-Criteria Decis. Anal. 20(5–6), 291–317 (2013)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Deb, K., Siegmund, F., Ng, A.H.C.: R-HV: a metric for computing hyper-volume for reference point based EMOs. In: Panigrahi, B.K., Suganthan, P.N., Das, S. (eds.) SEMCCO 2014. LNCS, vol. 8947, pp. 98–110. Springer, Cham (2015). doi:10.1007/978-3-319-20294-5_9. ISBN: 978-3-319-20293-8
Deb, K., Sundar, J., Bhaskara Rao, N., Chaudhur, S.: Reference point based multi-objective optimization using evolutionary algorithms. Int. J. Comput. Intell. Res. 2(3), 273–286 (2006)
Deb, K., Sinha, A., Korhonen, P.J., Wallenius, J.: An interactive evolutionary multi-objective optimization method based on progressively approximated value functions. IEEE Trans. Evol. Comput. 14(5), 723–739 (2010)
Enginarlar, E., Li, J., Meerkov, S.M.: How lean can lean buffers be? IIE Trans. 37(4), 333–342 (2005)
Li, K., Deb, K.: Performance Assessment for Preference-Based Evolutionary Multi-Objective Optimization Using Reference Points (2016, submitted to journal). http://www.egr.msu.edu/kdeb/papers/c2016001.pdf
López-Jaimes, A., Arias Montaño, A., Coello Coello, C.A.: Preference incorporation to solve many-objective airfoil design problems. In: Proceedings of the Congress on Evolutionary Computation 2011, New Orleans, USA, pp. 1605–1612 (2011). ISBN: 978-1-4244-7834-7
López-Jaimes, A., Arias Montaño, A., Coello Coello, C.A.: Including preferences into a multiobjective evolutionary algorithm to deal with many-objective engineering optimization problems. Inf. Sci. 277, 1–20 (2014). ISSN: 0020–0255
Siegmund, F., Ng, A.H.C., Deb, K.: Finding a preferred diverse set of pareto-optimal solutions for a limited number of function calls. In: Proceedings of the IEEE Congress on Evolutionary Computation 2012, Brisbane, Australia, pp. 2417–2424 (2012). ISBN: 978–1-4673-1508-1
Siegmund, F., Ng, A.H.C., Deb, K.: A comparative study of dynamic resampling strategies for guided evolutionary multi-objective optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation 2013, Cancún, Mexico, pp. 1826–1835 (2013). ISBN: 978-1-4799-0454-9
Siegmund, F., Ng, A.H.C., Deb, K.: Hybrid dynamic resampling for guided evolutionary multi-objective optimization. In: Proceedings of the 8th International Conference on Evolutionary Multi-Criterion Optimization, Guimarães, Portugal, pp. 366–380 (2015). ISBN: 978-3-319-15934-8
Siegmund, F., Ng, A.H.C., Deb, K.: A ranking and selection strategy for preference-based evolutionary multi-objective optimization of variable-noise problems. In: Proceedings of the Congress on Evolutionary Computation WCCI-CEC 2016, Vancouver, Canada, pp. 3035–3044, July 2016. ISBN: 978-1-5090-0623-6
Stump, G., Simpson, T.W., Donndelinger, J.A., Lego, S., Yukish, M.: Visual steering commands for trade space exploration: user-guided sampling with example. J. Comput. Inf. Sci. Eng. 9(4), 1–10 (2009). 044501, ISSN: 1530–9827
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)
Zitzler, E., Thiele, L.: Multiobjective optimization using evolutionary algorithms — a comparative case study. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 292–301. Springer, Heidelberg (1998). doi:10.1007/BFb0056872
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This study was partially funded by the Knowledge Foundation, Sweden, through the IDSS project. The authors gratefully acknowledge their provision of research funding.
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Siegmund, F., Ng, A.H.C., Deb, K. (2017). A Comparative Study of Fast Adaptive Preference-Guided Evolutionary Multi-objective Optimization. In: Trautmann, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science(), vol 10173. Springer, Cham. https://doi.org/10.1007/978-3-319-54157-0_38
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DOI: https://doi.org/10.1007/978-3-319-54157-0_38
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