Abstract
This work analyses the behavior and compares the performance of MOEA/D, IBEA using the binary additive \(\varepsilon \) and the hypervolume difference indicators, and A\(\varepsilon \)S\(\varepsilon \)H as representative algorithms of decomposition, indicators, and \(\varepsilon \)-dominance based approaches for many-objective optimization. We use small MNK-landscapes to trace the dynamics of the algorithms generating high-resolution approximations of the Pareto optimal set. Also, we use large MNK-landscapes to analyze their scalability to larger search spaces.
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
Similar content being viewed by others
References
Hughes, E.: MSOPS-II: a general-purpose many-objective optimiser. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC ), pp. 3944–3951 (2007)
Zhang, Q., Li, H.: MOEA/D: a multi-objective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
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 2004. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004)
Beume, N., Naujoks, B., Emmerich, M.: SMS-EMOA: multiobjective selection based on dominated hypervolume. Eur. J. Oper. Res. 181(3), 1653–1669 (2007)
Igel, C., Hansen, N., Roth, S.: Covariance matrix adaptation for multi-objective optimization. Evol. Comput. 15(1), 1–28 (2007)
Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining convergence and diversity in evolutionary multi-objective optimization. Evol. Comput. 10(3), 263–282 (2002)
Hadka, D., Reed, P.: Borg: an auto-adaptive many-objective evolutionary computing framework. Evol. Comput. 2(2), 231–259 (2013)
Aguirre, H., Oyama, A., Tanaka, K.: Adaptive \(\epsilon \)-sampling and \(\epsilon \)-hood for evolutionary many-objective optimization. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds.) EMO 2013. LNCS, vol. 7811, pp. 322–336. Springer, Heidelberg (2013)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. KanGAL report, 200001 (2000)
Aguirre, H., Tanaka, K.: Insights on properties of multi-objective MNK-landscapes. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 196–203. IEEE Service Center (2004)
Aguirre, H., Liefooghe, A., Verel, S., Tanaka, K.: An analysis on selection for high-resolution approximations in many-objective optimization. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds.) PPSN 2014. LNCS, vol. 8672, pp. 487–497. Springer, Heidelberg (2014)
Martínez, S.Z., Aguirre, H., Tanaka, K., Coello, C.: On the low-dyscrepancy sequences and their use in MOEA/D for high dimensionality objective spaces. In: Proceedings of the IEEE Congress on Evolutionary Computation, IEEE Press (2015) (to appear)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Aguirre, H., Zapotecas, S., Liefooghe, A., Verel, S., Tanaka, K. (2016). Approaches for Many-Objective Optimization: Analysis and Comparison on MNK-Landscapes. In: Bonnevay, S., Legrand, P., Monmarché, N., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2015. Lecture Notes in Computer Science(), vol 9554. Springer, Cham. https://doi.org/10.1007/978-3-319-31471-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-31471-6_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-31470-9
Online ISBN: 978-3-319-31471-6
eBook Packages: Computer ScienceComputer Science (R0)