D2MOPSO: Multi-Objective Particle Swarm Optimizer Based on Decomposition and Dominance

  • Noura Al Moubayed
  • Andrei Petrovski
  • John McCall
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7245)

Abstract

D2MOPSO is a multi-objective particle swarm optimizer that incorporates the dominance concept with the decomposition approach. Whilst decomposition simplifies the multi-objective problem (MOP) by rewriting it as a set of aggregation problems, solving these problems simultaneously, within the PSO framework, might lead to premature convergence because of the leader selection process which uses the aggregation value as a criterion. Dominance plays a major role in building the leader’s archive allowing the selected leaders to cover less dense regions avoiding local optima and resulting in a more diverse approximated Pareto front. Results from 10 standard MOPs show D2MOPSO outperforms two state-of-the-art decomposition based evolutionary methods.

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References

  1. 1.
    Zhang, Q., Li, H.: MOEA/D: A multi-objective evolutionary algorithm based on decomposition. IEEE Trans. on Evolutionary Computation 11(6), 712–731 (2007)CrossRefGoogle Scholar
  2. 2.
    Li, H., Zhang, Q.: Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-ii. IEEE Trans. on Evolutionary Computation 13(2), 284–302 (2009)CrossRefGoogle Scholar
  3. 3.
    Awwad Shiekh Hasan, B., Gan, J.Q., Zhang, Q.: Multi-objective evolutionary methods for channel selection in brain-computer interfaces: some preliminary experimental results. In: WCCI. IEEE (2010)Google Scholar
  4. 4.
    Wang, Z., Durst, G.L., Eberhart, R.C., Boyd, D.B., Ben Miled, Z.: Particle swarm optimization and neural network application for qsar. In: Parallel and Distributed Processing Symposium, International, vol. 10, p. 194 (2004)Google Scholar
  5. 5.
    Jaishia, B., Ren, W.: Finite element model updating based on eigenvalue and strain. Mechanical Systems and Signal Processing 21(5), 2295–2317 (2007)CrossRefGoogle Scholar
  6. 6.
    Al Moubayed, N., Petrovski, A., McCall, J.: Multi-objective optimisation of cancer chemotherapy using smart pso with decomposition. In: 3rd IEEE Sym. Comp. Intel. IEEE (2011)Google Scholar
  7. 7.
    Reyes-Sierra, M., Coello, C.A.C.: Multi-objective particle swarm optimizers: A survey of the state-of-the-art. International Journal of Computational Intelligence Research 2(3), 287–308 (2006)MathSciNetGoogle Scholar
  8. 8.
    Baltar, A.M., Fontane, D.G.: A generalized multiobjective particle swarm optimization solver for spreadsheet models: application to water quality. In: The Twenty Sixth Annual American Geophysical Union Hydrology Days (2006)Google Scholar
  9. 9.
    Hassan, R., Cohanim, B., de Weck, O., Venter, G.: A comparison of particle swarm optimization and the genetic algorithm. In: Structural Dynamics and Materials, Texas, USA (2005)Google Scholar
  10. 10.
    Al Moubayed, N., Petrovski, A., McCall, J.: A novel smart multi-objective particle swarm optimisation using decomposition. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI, Part II. LNCS, vol. 6239, pp. 1–10. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Martínez, S.Z., Coello, C.A.C.: A multi-objective particle swarm optimizer based on decomposition. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011. ACM (2011)Google Scholar
  12. 12.
    Sierra, M.R., Coello, C.A.C.: Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ε-Dominance. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 505–519. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm intelligence. Morgan Kaufmann, San Francisco (2001)Google Scholar
  14. 14.
    Coello, C.A.C., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems, 2nd edn. Genetic and Evolutionary Computation. Springer, New York (2007)MATHGoogle Scholar
  15. 15.
    El-Ghazali, T.: Metaheuristics: from design to implementation. John Wiley & Sons (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Noura Al Moubayed
    • 1
  • Andrei Petrovski
    • 1
  • John McCall
    • 1
  1. 1.School of ComputingRobert Gordon UniversityAberdeenUK

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