Abstract
Multi-objective evolutionary algorithms are increasingly being investigated to solve many-objective optimization problems. However, most algorithms recently proposed for many-objective optimization cannot find Pareto optimal solutions with good properties on convergence, spread, and distribution. Often, the algorithms favor one property at the expense of the other. In addition, in some applications it takes a very long time to evaluate solutions, which prohibits running the algorithm for a large number of generations. In this work to obtain good representations of the Pareto optimal set we investigate a large population MOEA, which employs adaptive ε-box dominance for selection and neighborhood recombination for variation, using a very short number of generations to evolve the population. We study the performance of the algorithm on some functions of the DTLZ family, showing the importance of using larger populations to search on many-objective problems and the effectiveness of employing adaptive ε-box dominance with neighborhood recombination that take into account the characteristics of many-objective landscapes.
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
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester (2001)
Coello, C., Van Veldhuizen, D., Lamont, G.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, Boston (2002)
Aguirre, H., Tanaka, K.: Insights on Properties of Multi-objective MNK-Landscapes. In: Proc. 2004 IEEE Congress on Evolutionary Computation, pp. 196–203. IEEE Service Center (2004)
Aguirre, H., Tanaka, K.: Working Principles, Behavior, and Performance of MOEAs on MNK-Landscapes. European Journal of Operational Research 181(3), 1670–1690 (2007)
Sato, H., Aguirre, H.E., Tanaka, K.: Genetic Diversity and Effective Crossover in Evolutionary Many-objective Optimization. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 91–105. Springer, Heidelberg (2011)
Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary Many-Objective Optimization: A Short Review. In: In Proc. IEEE Congress on Evolutionary Computation (CEC 2008), pp. 2424–2431. IEEE Press (2008)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable Multi-Objective Optimization Test Problems. In: Proc. 2002 Congress on Evolutionary Computation, pp. 825–830. IEEE Service Center (2002)
Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining Convergence and Diversity in Evolutionary Multi-objective Optimization. Evolutionary Computation 10(3), 263–282 (Fall 2002)
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.: Adaptive ε-Ranking on Many-Objective Problems. Evolutionary Intelligence 2(4), 183–206 (2009)
Purshouse, R.C., Fleming, P.J.: Evolutionary Many-Objective Optimization: An Exploratory Analysis. In: Proc. IEEE CEC 2003, pp. 2066–2073 (2003)
Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization:Methods and Applications. PhD thesis, Swiss Federal Institute of Technology, Zurich (1999)
Fonseca, C., Paquete, L., López-Ibáñez, M.: An Improved Dimension-sweep Algorithm for the Hypervolume Indicator. In: Proc. 2006 IEEE Congress on Evolutionary Computation, pp. 1157–1163. IEEE Service Center (2006)
Ikeda, K., Kita, H., Kobayashi, S.: Failure of Pareto-based MOEAs: does non-dominated really mean near to optimal? In: Proc. of the 2001 Congress on Evolutionary Computation, vol. 2, pp. 957–962. IEEE Service Center (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kowatari, N., Oyama, A., Aguirre, H.E., Tanaka, K. (2012). A Study on Large Population MOEA Using Adaptive ε-Box Dominance and Neighborhood Recombination for Many-Objective Optimization. In: Hamadi, Y., Schoenauer, M. (eds) Learning and Intelligent Optimization. LION 2012. Lecture Notes in Computer Science, vol 7219. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-34413-8_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34412-1
Online ISBN: 978-3-642-34413-8
eBook Packages: Computer ScienceComputer Science (R0)