Abstract
In this paper, we study iSMS-EMOA, a recently proposed approach that improves the well-known S metric selection Evolutionary Multi-Objective Algorithm (SMS-EMOA). These two indicator-based multi-objective evolutionary algorithms rely on hypervolume contributions to select individuals. Here, we propose to define a probability of using a randomly selected individual within the iSMS-EMOA’s selection scheme. In order to calibrate the value of such probability, we use the EVOCA tuner. Our preliminary results indicate that we are able to save up to 33% of computations of the contribution to hypervolume with respect to the original iSMS-EMOA, without any significant quality degradation in the solutions obtained. In fact, in some cases, the approach proposed here was even able to improve the quality of the solutions obtained by the original iSMS-EMOA.
This work is supported by the collaboration project Conacyt-Conicyt 2010-199. The last author acknowledges support from project B330.261.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bringmann, K., Friedrich, T.: Approximating the least hypervolume contributor: NP-hard in general, but fast in practice. Theoretical Computer Science 425, 104–116 (2012)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable Test Problems for Evolutionary Multiobjective Optimization. In: Abraham, A., Jain, L., Goldberg, R. (eds.) Evolutionary Multiobjective Optimization. Theoretical Advances and Applications, pp. 105–145. Springer, USA (2005)
Emmerich, M., Beume, N., Naujoks, B.: An EMO Algorithm Using the Hypervolume Measure as Selection Criterion. In: Coello, C.A.C., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 62–76. Springer, Heidelberg (2005)
Farina, M., Amato, P.: On the Optimal Solution Definition for Many-criteria Optimization Problems. In: Proceedings of the NAFIPS-FLINT International Conference 2002, Piscataway, New Jersey, pp. 233–238. IEEE Service Center (June 2002)
Fleischer, M.: The Measure of Pareto Optima Applications to Multi-objective Metaheuristics. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 519–533. Springer, Heidelberg (2003)
Huband, S., Hingston, P., Barone, L., While, L.: A Review of Multiobjective Test Problems and a Scalable Test Problem Toolkit. IEEE Transaction on Evolutionary Computation 10(5), 477–506 (2006)
Menchaca-Mendez, A., Coello, C.A.C.: A New Selection Mechanism Based on Hypervolume and its Locality Property. In: 2013 IEEE Congress on Evolutionary Computation (CEC 2013), Cancún, México, June 20-23, pp. 924–931. IEEE Press (2013)
Riff, M.-C., Montero, E.: A new algorithm for reducing metaheuristic design effort. In: IEEE Congress on Evolutionary Computation (CEC 2013), Cancún, México, pp. 3283–3290 (June 2013)
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)
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation 7(2), 117–132 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Menchaca-Mendez, A., Montero, E., Riff, MC., Coello, C.A.C. (2014). A More Efficient Selection Scheme in iSMS-EMOA. In: Bazzan, A., Pichara, K. (eds) Advances in Artificial Intelligence -- IBERAMIA 2014. IBERAMIA 2014. Lecture Notes in Computer Science(), vol 8864. Springer, Cham. https://doi.org/10.1007/978-3-319-12027-0_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-12027-0_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12026-3
Online ISBN: 978-3-319-12027-0
eBook Packages: Computer ScienceComputer Science (R0)