Abstract
In this paper, we present a parallel version of a multi-objective evolutionary algorithm that incorporates some coevolutionary concepts. Such an algorithm was previosly developed by the authors. Two approaches were adopted to parallelize our algorithm (both of them based on a master-slave scheme): one uses Pthreads (shared memory) and the other one uses MPI (distributed memory). We conduct a small comparative study to analyze the impact that the parallelization has on performance. Our results indicate that both parallel versions produce important improvements in the execution times of the algorithm (with respect to the serial version) while keeping the quality of the results obtained.
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Coello Coello, C.A., Van Veldhuizen, D.A., Lamont, G.B.: EvolutionaryAlgorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York (2002) ISBN 0-3064-6762-3
Coello Coello, C.A., Reyes Sierra, M.: A Coevolutionary Multi-Objective Evolutionary Algorithm. In: Proceedings of the Congress on Evolutionary Computation, IEEE Press, Los Alamitos (2003) (accepted for publication)
Fonseca, C.M., Fleming, P.J.: Genetic algorithms for multiobjective optimization: formulation, discussion and generalization. In: Forrest, S. (ed.) Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, California, University of Illinois at Urbana-Champaign, pp. 416–423. Morgan Kauffman Publishers, San Francisco (1993)
Knowles, J.D., Corne, D.W.: Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation 8, 149–172 (2000)
Paredis, J.: Coevolutionary algorithms. In: Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.) The Handbook of Evolutionary Computation, pp. 225–238. Institute of Physics Publishing and Oxford University Press (1998) (1st suppl.)
Potter, M., Jong, K.D.: A cooperative coevolutionary approach to function optimization. In: Proceedings from the Fifth Parallel Problem Solving from Nature, Jerusalem, Israel, pp. 530–539. Springer, Heidelberg (1994)
Van Veldhuizen, D.A.: Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovations. PhD thesis, Department of Electrical and Computer Engineering. Graduate School of Engineering. Air Force Institute of Technology, Wright-Patterson AFB, Ohio (1999)
Van Veldhuizen, D.A., Lamont, G.B.: Multiobjective Evolutionary Algorithm Research: A History and Analysis. Technical Report TR-98-03, Department of Electrical and Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, Wright-Patterson AFB, Ohio (1998)
Van Veldhuizen, D.A., Lamont, G.B.: On Measuring Multiobjective Evolutionary Algorithm Performance. In: Congress on Evolutionary Computation, vol. 1, pp. 204–211. IEEE Service Center, Piscataway (2000)
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Coello Coello, C.A., Reyes Sierra, M. (2004). A Study of the Parallelization of a Coevolutionary Multi-objective Evolutionary Algorithm. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_71
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DOI: https://doi.org/10.1007/978-3-540-24694-7_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21459-5
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