Abstract
Multi-objective Evolutionary Algorithms (MOEA) are used to solve complex multi-objective problems. As the number of objectives increases, Pareto-based MOEAs are unable to maintain the same effectiveness showed for two or three objectives. Therefore, as a way to ameliorate this performance degradation several authors proposed preference-based methods as an alternative to Pareto based approaches. On the other hand, parallelization has shown to be useful in evolutionary optimizations. A central aspect for the parallelization of evolutionary algorithms is the population partitioning approach. Thus, this paper presents a new parallelization approach based on clustering by the shape of objective vectors to deal with many-objective problems. The proposed method was compared with random and \(k\)-means clustering approaches using a multi-threading framework in parallelization of the NSGA-II and six variants using preference-based relations for fitness assignment. Executions were carried-out for the DTLZ problem suite, and the obtained solutions were compared using the generational distance metric. Experimental results show that the proposed shape-based partition achieves competitive results when comparing to the sequential and to other partitioning approaches.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Branke, J., Schmeck, H., Deb, K., Reddy, M.: Parallelizing multi-objective evolutionary algorithms: cone separation. In: 2004 Congress on Evol. Comput., vol. 2, pp. 1952–1957. IEEE, Portland (2004)
Cheshmehgaz, H.R., Haron, H., Sharifi, A.: The review of multiple evolutionary searches and multi-objective evolutionary algorithms. Artificial Intelligence Review, 1–33 (2013)
Coello Coello, C.A., Lamont, G., Van Veldhuizen, D.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer, New York (2007). ISBN: 978-0-387-33254-3
Deb, K.: Multi-objective optimization using evolutionary algorithms. Wiley (2001)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. on Evol. Comput. (2002)
Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable multi-objective optimization test problems. In: Proc. of the 2002 Congr. on Evol. Comput. (2002)
Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1(1), 3–18 (2011)
di Pierro, F., Khu, S., Savić, D.: An investigation on Preference Order ranking scheme for multiobjective evolutionary optimization. IEEE Trans. on Evol. Comput. (2007)
Drechsler, N., Drechsler, R., Becker, B.: Multi-objective optimisation based on relation favour. In: First Int. Conf. on Evol. Multi-Criterion Optim. Springer (2001)
Egecioglu, Ö.: Parametric approximation algorithms for high-dimensional euclidean similarity. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 79–90. Springer, Heidelberg (2001)
Fagin, R., Kumar, R., Sivakumar, D.: Comparing top k lists. In: Proc. of the 14th Annual ACM-SIAM Symp. on Discrete Algorithms. p. 36. SIAM (2003)
Farina, M., Amato, P.: On the Optimal Solution Definition for Many-criteria Optimization Problems. In: Proc. of the NAFIPS-FLINT Int. Conf. 2002, pp. 233–238. IEEE Service Center (2002)
Hiroyasu, T., Miki, M., Watanabe, S.: The new model of parallel genetic algorithm in multi-objective optimization problems: divided range multi-objective genetic algorithm. In: 2000 Congress on Evol. Comput., vol. 1, pp. 333–340. IEEE, New Jersey (2000)
Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary many-objective optimization: A short review. In: 2008 IEEE Congr. on Evol. Comput. (2008)
Jaimes, A.L., Coello Coello, C.A.: Applications of parallel platforms and models in evolutionary multi-objective optimization. In: Lewis, A., Mostaghim, S., Randall, M. (eds.) Biologically-Inspired Optimisation Methods. SCI, vol. 210, pp. 23–49. Springer, Heidelberg (2009)
Köppen, M., Yoshida, K.: Substitute distance assignments in NSGA-II for handling many-objective optimization problems. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 727–741. Springer, Heidelberg (2007)
von Lücken, C., Barán, B., Brizuela, C.: A survey on multi-objective evolutionary algorithms for many-objective problems. Comput. Optim. and Appl. 1(1), 1–50 (2014)
Negro, F.D.T., Ortega, J., Ros, E., Mota, S., Paechter, B., Martín, J.M.: PSFGA: parallel processing and evolutionary comput. for multiobjective optim. Parallel Comput. (2004)
Streichert, F., Ulmer, H., Zell, A.: Parallelization of multi-objective evolutionary algorithms using clustering algorithms. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 92–107. Springer, Heidelberg (2005)
Sülflow, A., Drechsler, N., Drechsler, R.: Robust multi-objective optimization in high dimensional spaces. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 715–726. Springer, Heidelberg (2007)
Talbi, E.-G., Mostaghim, S., Okabe, T., Ishibuchi, H., Rudolph, G., Coello Coello, C.A.: Parallel approaches for multiobjective optimization. In: Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds.) Multiobjective Optimization. LNCS, vol. 5252, pp. 349–372. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
von Lücken, C., Brizuela, C., Barán, B. (2015). Clustering Based Parallel Many-Objective Evolutionary Algorithms Using the Shape of the Objective Vectors. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C. (eds) Evolutionary Multi-Criterion Optimization. EMO 2015. Lecture Notes in Computer Science(), vol 9019. Springer, Cham. https://doi.org/10.1007/978-3-319-15892-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-15892-1_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-15891-4
Online ISBN: 978-3-319-15892-1
eBook Packages: Computer ScienceComputer Science (R0)