A Study of the Parallelization of the Multi-Objective Metaheuristic MOEA/D

  • Antonio J. Nebro
  • Juan J. Durillo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6073)


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.


Multi-Objective Optimization Metaheuristics Parallelism Multi-core processors 


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  1. 1.
    Andrews, G.R.: Multithreaded, Paralle, and Distributed Programming. Addison-Wesley, Reading (2000)Google Scholar
  2. 2.
    Blum, C., Roli, A.: Metaheuristics in Combinatorial Optimization: Overview and Conceptual Comparison. ACM Computing Surveys 35(3), 268–308 (2003)CrossRefGoogle Scholar
  3. 3.
    Coello Coello, C.A., Lamont, G.B., Van Veldhuizen, D.A.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Springer, New York (2007), ISBN 978-0-387-33254-3zbMATHGoogle Scholar
  4. 4.
    Deb, K.: Multi-objective optimization using evolutionary algorithms. John Wiley & Sons, Chichester (2001)zbMATHGoogle Scholar
  5. 5.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  6. 6.
    Demšar, J.: Statistical Comparisons of Classifiers over Multiple Data Sets. J. Mach. Learn. Res. 7, 1–30 (2006)MathSciNetGoogle Scholar
  7. 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
  8. 8.
    Glover, F.W., Kochenberger, G.A.: Handbook of Metaheuristics. Kluwer, Dordrecht (2003)zbMATHGoogle Scholar
  9. 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
  10. 10.
    Knowles, J.D., Corne, D.W.: Approximating the nondominated front using the pareto archived evolution strategy. Evolutionary Computation 8(2), 149–172 (2000)CrossRefGoogle Scholar
  11. 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
  12. 12.
    Li, H., Zhang, Q.: Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation 2(12), 284–302 (2009)CrossRefGoogle Scholar
  13. 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
  14. 14.
    Nebro, A.J., Durillo, J.J., Luna, F., Dorronsoro, B., Alba, E.: Mocell: A cellular genetic algorithm for multiobjective optimization. Internatinal Journal of Intelligent Systems 24(7), 726–746 (2009)zbMATHCrossRefGoogle Scholar
  15. 15.
    Weise, T., Zapf, M., Chiong, R., Nebro, A.J.: Why is optimization difficult? In: Chiong, R. (ed.) Nature-Inspired Algorithms for Optimisation. SCI, vol. 193, pp. 1–50. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  16. 16.
    Zhang, Q., Li, H.: Moea/d: A multi-objective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation 1(6), 712–731 (2007)CrossRefGoogle Scholar
  17. 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. 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

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Antonio J. Nebro
    • 1
  • Juan J. Durillo
    • 1
  1. 1.Department of Computer ScienceUniversity of MálagaSpain

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