Advertisement

Clustering-Based Leaders’ Selection in Multi-Objective Particle Swarm Optimisation

  • Noura Al Moubayed
  • Andrei Petrovski
  • John McCall
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6936)

Abstract

Clustering-based Leaders’ Selection (CLS) is a novel approach for leaders selection in multi-objective particle swarm optimisation. Both objective and solution spaces are clustered. An indirect mapping between clusters in both spaces is defined to recognize regions with potentially better solutions. A leaders archive is built which contains representative particles of selected clusters in the objective and solution spaces. The results of applying CLS integrated with OMOPSO on seven standard multi-objective problems, show that clustering based leaders selection OMOPSO (OMOPSO/C) is highly competitive compared to the original algorithm.

Keywords

Evolutionary Computation Multi-Objective Particle Swarm Optimisation Leaders’ Selection Density Based Spatial Clustering Principal Component Analysis Domination OMOPSO 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Reyes-Sierra, M., Coello, C.A.C.: Multi-objective particle swarm optimizers: A survey of the state-of-the-art. Int. J. Comp. Intel. Res. 2(3), 287–308 (2006)MathSciNetGoogle Scholar
  2. 2.
    Wang, Z., Durst, G.L., Eberhart, R.C., Boyd, D.B., Ben Miled, Z.: Particle swarm optimization and neural network application for qsar. In: Int. Par. Dist. Proc. Sym., vol. 10, p.194 (2004)Google Scholar
  3. 3.
    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, Los Alamitos (2011)Google Scholar
  4. 4.
    Knowles, J., Corne, D.W.: Approximating the nondominated front using the pareto archived evolution strategy. Evo. Comp. 8, 149–172 (2000)CrossRefGoogle Scholar
  5. 5.
    Deb, K., Goldberg, D.E.: An investigation of niche and species formation in genetic function optimization. In: 3rd Int. Conf. Gen. Alg. Morgan Kaufmann Publishers Inc., San Francisco (1989)Google Scholar
  6. 6.
    Laumanns, M., Thiele, L., Deb, K., Zitzler, E.: Combining convergence and diversity in evolutionary multiobjective optimization. Evo. Comp. 10(3), 263–282 (2002)CrossRefGoogle Scholar
  7. 7.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: Nsga-ii. IEEE Trans. Evo. Comp. 6(2), 181–197 (2002)Google Scholar
  8. 8.
    Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Trans. Evo. Comp. 4(3), 257–271 (1999)CrossRefGoogle Scholar
  9. 9.
    Wang, Y., Dang, C., Li, H., Han, L., Wei, J.: A clustering multi-objective evolutionary algorithm based on orthogonal and uniform design. In: Proc. Eleventh Conf. Congress on Evo. Comp., CEC 2009, pp. 2927–2933. IEEE Press, Los Alamitos (2009)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. LNCS, vol. 6239, pp. 1–10. Springer, Heidelberg (2010)Google Scholar
  11. 11.
    Parsopoulos, K.E., Tasoulis, D.K., Vrahatis, M.N.: Multiobjective optimization using parallel vector evaluated particle swarm optimization. In: Proc. IASTED. ACTA Press (2004)Google Scholar
  12. 12.
    Reyes-Sierra, M., 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.
    Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. 2nd Int. Conf. Know. Disc. Data Min. (1996)Google Scholar
  14. 14.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  15. 15.
    Basu, S., Banerjee, A., Mooney, R.: Semi-supervised clustering by seeding. In: Proc. 19th Int. Conf. Machine Learning (2002)Google Scholar
  16. 16.
    Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, Norwell (2002)zbMATHGoogle Scholar
  17. 17.
    El-Ghazali, T.: Metaheuristics: from design to implementation. John Wiley & Sons, Chichester (2009)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Noura Al Moubayed
    • 1
  • Andrei Petrovski
    • 1
  • John McCall
    • 1
  1. 1.Robert Gordon UniversityAberdeenUK

Personalised recommendations