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A New Cooperative Evolutionary Multi-Swarm Optimizer Algorithm Based on CUDA Architecture Applied to Engineering Optimization

  • Daniel Leal Souza
  • Otávio Noura Teixeira
  • Dionne Cavalcante Monteiro
  • Roberto Célio Limão de Oliveira
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 23)

Abstract

This paper presents a new Cooperative Evolutionary Multi-Swarm Optimization Algorithm (CEMSO-GPU) based on CUDA parallel architecture applied to solve engineering problems. The focus of this approach is: the use of the concept of master/slave swarm with a mechanism of data sharing; and, the parallelism method based on the paradigm of General Purpose Computing on Graphics Processing Units (GPGPU) with CUDA architecture, brought by NVIDIA corporation. All these improvements were made aiming to produce better solutions in fewer iterations of the algorithm and to improve the search for best results. The algorithm was tested for some well-known engineering problems (WBD, ATD, MWTCS, SRD-11) and the results compared to other approaches.

Keywords

PSO EPSO GPGPU GPU CUDA NVIDIA  Swarm Cooperative Parallel Hetergeneus computing  Particle 

Notes

Acknowledgments

This work is supported financially by Research Support Foundation of Par(FAPESPA) and Federal University of Pará (UFPA).

References

  1. 1.
    Bastos Filho, C.J.A., Caraciolo, M.P., Miranda, P.B.C., Carvalho, D.F: Multi ring PSO. In: The 10th Brazilian Symposium on Neural Networks (SBRN’2008), pp. 111–116 (2008)Google Scholar
  2. 2.
    Lopes, H.S., Takahashi, R.H.C.: Computação Evolucionária em Problemas de Engenharia, 1st edn. Ed. OMNIPAX, (2011) (in portuguese)Google Scholar
  3. 3.
    Miranda, V., Fonseca, N.: EPSO—Evolutionary particle swarm optimization, a new algorithm with applications in power systems. In: Transmission and Distribution Conference and Exhibition 2002: Asia Pacific. IEEE/PES, vol. 2, pp. 745–750 (2002)Google Scholar
  4. 4.
    Van Den Bergh, H., Engelbrecht, A.P.: A Cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8, 225–239 (2004)Google Scholar
  5. 5.
    Kirk, D.B., Hwu, W.M.: Programming Massively Parallel Processors a Hands-on Approach, 1st edn. Elsevier, Oxford (2010)Google Scholar
  6. 6.
    Solomon, S., Thulasiraman, P., Thulasiraman, R.: Collaborative multi-swarm PSO for task matching using graphics processing units. In: GECCO ’11 Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, vol. 2, pp. 1563–1570 (2011)Google Scholar
  7. 7.
    Mussi, L., Nashed, Y.S.G., Cagnoni, S.: GPU-based asynchronous particle swarm optimization. In: GECCO ’11 Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, vol. 2, pp. 1555–1562 (2011)Google Scholar
  8. 8.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  9. 9.
    Eberhart, R., Shi, Y.: Comparing inertia weights and constriction factors. In: Proceedings of the Congress on Evolutionary Computing, pp. 84–89 (2000)Google Scholar
  10. 10.
    Eberhart, R., Shi, Y.: A modified particle swarm optimizer. In: IEEE International Conference of Evolutionary Computation, pp. 69–73. Anchorage, Alaska (1998)Google Scholar
  11. 11.
    Leite, H., Barros, J., Miranda, V.: The evolutionary algorithm EPSO to coordinate directional overcurrent relay. In: 10th IET International Conference Developments in Power System Protection (DPSP 2010) Managing the Change, pp. 1–5 (2010)Google Scholar
  12. 12.
    Sanders, J., Kandrot, E.: CUDA by Example: An Introduction to General-Purpose GPU Programming. Addison-Wesley Professional, New York (2010)Google Scholar
  13. 13.
    Niu, B., Zhu, Y., He, X.: Multi-population cooperative particle swarm optimization. In: Proceedings of the European Conference on Artificial Life, pp. 874–883 (2005)Google Scholar
  14. 14.
    Souza, D.L., Monteiro, G.D., Martins, T.C., Teixeira, O.N., Dmitriev, V.A.: PSO-GPU: accelerating particle swarm optimization. In: CUDA-Based Graphics Processing Units. GECCO 2011, ACM Digital Library, pp. 837–838 (2011)Google Scholar
  15. 15.
    Teixeira, O.N., Lobato, W.A.L.L., Yanaguibashi, H.S., Cavalcante, R.V., Silva, D.J.A., Oliveira, R.C.L.: Algoritmo Genético com Interação Social na Resolução de Problemas de Otimização Global com Restrições (in portuguese), Computação Evolucionária em Problemas de Engenharia, Ed. OMNIPAX, 1st edn. pp. 197–223, (2011) (in portuguese)Google Scholar
  16. 16.
    He, Q., Wang, L.: An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng. Appl. Artif. Intell. 20, 89–99 (2007)Google Scholar
  17. 17.
    Mezura-Montes, E.: Coello Coello, C.: Useful infeasible solutions in engineering optimization with evolutionary algorithms. In: Proceedings of the 4th Mexican International Conference on Artificial Intelligence, MICAI 2005, Lecture Notes on Artificial Intelligence No. 3789, pp. 652–662 (2005)Google Scholar
  18. 18.
    Hsu, Y.L., Liu, T.C.: Developing a fuzzy proportional-derivative controller optimization engine for engineering design optimization problems. Eng. Optim. 39(6), 679–700 (2007)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Golinski, J.: An adaptive optimization system applied to machine synthesis. Mech. Mach. Synth. 8(4), 419–436 (1973)CrossRefGoogle Scholar
  20. 20.
    Brajevic, I., Tuba, M., Subotic, M.: Improved artificial bee colony algorithm for constrained problems. In: Proceedings of the 11th WSEAS International Conference on Neural Networks, Fuzzy Systems and Evolutionary Computing, Stevens Point, USA: WSEAS, pp. 185–190 (2010)Google Scholar
  21. 21.
    Cagnina, L., Esquivel, S., Coello, C.: Solving engineering optimization problems with the simple constrained particle swarm optimizer. Informatica 32(3), 319–326 (2008)MATHGoogle Scholar
  22. 22.
    Coello C., Montes, E.: Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv. Eng. Inform. 16, 193–203 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Daniel Leal Souza
    • 2
    • 1
  • Otávio Noura Teixeira
    • 1
    • 3
  • Dionne Cavalcante Monteiro
    • 2
  • Roberto Célio Limão de Oliveira
    • 3
  1. 1.Laboratory of Natural Computing (LCN) Area of Exact and Natural Sciences (ACET)University Centre of Pará (CESUPA)BelémBrazil
  2. 2.Institute of Exact and Natural Sciences (ICEN), Laboratory of Applied Artificial Intelligence (LAAI)Federal University of Pará (UFPA)BelémBrazil
  3. 3.Institute of Technology (ITEC), Post-Graduate Program in Electrical Engineering (PPGEE)Federal University of Para (UFPA)BelémBrazil

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