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1,1

Experimental comparison of many-objective evolutionary preference-based methods in a parallel framework

doi: 10.6062/jcis.2016.07.01.0103

(Free PDF)

Authors

Christian von Lücken, Carlos Brizuela and Benjamin BarĂ¡n

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 reproduce the same effectiveness showed for two or three objectives. Thus, several authors proposed preference-based methods as an alternative. On the other hand, parallelization has shown to be useful in evolutionary optimizations. This paper combines for the first time seven preference-based methods for many objective optimization in a multi-threading parallelization framework. Preference-based methods were used to replace the elitism procedure of the Non-dominated Sorting Genetic Algorithm II. Executions of each alternative were carried-out for the DTLZ-2 problem in a commodity multi-core platform. Obtained solutions were compared by different criteria, providing some insights into the improvements that the proposed combination may offer in many-objective optimization.

Keywords

Multi-objective evolutionary algorithms, many-objective optimization, parallel evolutionary qlgo- rithms, computational mathematics. 1

References

[1] Chapman, B., Jost G., Van Der Pas R., Kuck, D.J. Using OpenMP: portable shared memory parallel programming. The MIT Press, 2007.

[2]J. Coello Coello, C., Lamont, G., Van Veldhuizen, D. Evolutionary Algorithms for Solving Multi-Objective Problems. Springer, New York, second edition, 2007. ISBN 978-0-387-33254-3.

[3]Corne, D. and Knowles, J. Techniques for highly multiobjective optimisation: some nondominated points are better than others. In Proc. of the 9th Ann. Conf. on Genetic and Evol. Comput., GECCO '07, pages 773-780. ACM Press, 2007.

[4] Deb, K. Multi-objective optimization using evolutionary algorithms. Wiley, 2001.

[5] Deb, K., Pratap, A., Agarwal, S., Meyarivan, T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. on Evol. Comput., 2002. doi:10.1109/4235.996017

[6] Deb, K., Thiele, L., Laumanns, M., Zitzler, E. Scalable multi-objective optimization test problems. In Proc. of the 2002 Congr. on Evol. Comput., 2002. doi:10.1109/CEC.2002.1007032

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