A Study of the Parallelization of the Multi-Objective Metaheuristic MOEA/D
MOEA/D is a multi-objective metaheuristic which has shown a remarkable performance when solving hard optimization problems. In this paper, we propose a thread-based parallel version of MOEA/D designed to be executed on modern multi-core processors. Our interest is to study the potential benefits of the parallel approach in terms of speed-ups and the quality of the obtained Pareto front approximations when solving a benchmark composed of nine problems. The obtained results on two different multi-core based machines indicate that notable time reductions can be achieved. We have also found out that, with a few exceptions, there are not significant differences in terms of solution quality among the sequential MOEA/D and the parallel versions of it when using up to eight threads.
KeywordsMulti-Objective Optimization Metaheuristics Parallelism Multi-core processors
Unable to display preview. Download preview PDF.
- 1.Andrews, G.R.: Multithreaded, Paralle, and Distributed Programming. Addison-Wesley, Reading (2000)Google Scholar
- 7.Durillo, J.J., Nebro, A.J., Luna, F., Dorronsoro, B., Alba, E.: jMetal: a java framework for developing multi-objective optimization metaheuristics. Technical Report ITI-2006-10, Departamento de Lenguajes y Ciencias de la Computación, University of Málaga, E.T.S.I. Informática, Campus de Teatinos (2006)Google Scholar
- 9.Knowles, J., Thiele, L., Zitzler, E.: A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers. Technical Report 214, Computer Engineering and Networks Laboratory (TIK), ETH Zurich (2006)Google Scholar
- 11.Kukkonen, S., Lampinen, J.: GDE3: The third evolution step of generalized differential evolution. In: IEEE Congress on Evolutionary Computation (CEC 2005), pp. 443–450 (2005)Google Scholar
- 13.Nebro, A.J., Luna, F., Alba, E., Dorronsoro, B., Durillo, J.J., Beham, A.: AbYSS: Adapting Scatter Search to Multiobjective Optimization. IEEE Transactions on Evolutionary Computation 12(4) (August 2008)Google Scholar
- 17.Zhang, Q., Liu, W., Li, H.: The performance of a new version of moea/d on cec09 unconstrained mop test instances. Technical Report CES-491, School of CS & EE, University of Essex (2009)Google Scholar
- 18.Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. In: Giannakoglou, K., Tsahalis, D., Periaux, J., Papailou, P., Fogarty, T. (eds.) EUROGEN 2001. Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems, Athens, Greece, pp. 95–100 (2002)Google Scholar