Advertisement

A Fine-Grained Parallel Particle Swarm Optimization on Many-core and Multi-core Architectures

  • Nadia NedjahEmail author
  • Rogério de Moraes Calazan
  • Luiza de Macedo Mourelle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10421)

Abstract

Particle Swarm Optimization (PSO) is a stochastic metaheuristics yet very robust. Real-world optimizations require a high computational effort to converge to a viable solution. In general, parallel PSO implementations provide good performance, but this depends on the parallelization strategy as well as the number and/or characteristics of the exploited processors. In this paper, we propose a fine-grained paralellization strategy that focuses on the work done w.r.t. each of the problem dimensions and does it in parallel. Moreover, all particles act in parallel. This strategy is useful in computationally demanding optimization problems wherein the objective function has a very large number of dimensions. We map the computation onto three different parallel high-performance multiprocessor architectures, which are based on many and multi-core architectures. The performance of the proposed strategy is evaluated for four well-known benchmarks with high-dimension and different complexity. The obtained speedups are very promising.

References

  1. 1.
    Cádenas-Montes, M., Vega-Rodríguez, M.A., Rodríguez-Vázquez, J.J., Gómez-Iglesias, A.: Accelerating particle swarm algorithm with GPGPU. In: Proceedings of the 19th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, pp. 560–564. IEEE Press (2011)Google Scholar
  2. 2.
    Calazan, R.M., Nedjah, N., Mourelle, L.M.: Swarm grid: a proposal for high performance of parallel particle swarm optimization using GPGPU. In: Proceedings of the 4th International Symposium of IEEE Circuits and Systems in Latin America (LASCAS 2013), Cuzco, Peru. IEEE Computer Press, Los Alamitos (2013)Google Scholar
  3. 3.
    Calazan, R.M., Nedjah, N., Mourelle, L.M.: A massively parallel reconfigurable co-processor for computationally demanding particle swarm optimization. In: Proceedings of the 3rd International Symposium of IEEE Circuits and Systems in Latin America (LASCAS 2012), Cancun, Mexico. IEEE Computer Press, Los Alamitos (2012)Google Scholar
  4. 4.
    Calazan, R.M., Nedjah, N., Mourelle, L.M.: Parallel co-processor for PSO. Int. J. High Perform. Syst. Archit. 3(4), 233–240 (2011)CrossRefGoogle Scholar
  5. 5.
    Chapman, B., Jost, G., Van Der Pas, R.: Using OpenMP: Portable Shared Memory Parallel Programming, vol. 10. MIT Press, England (2008)Google Scholar
  6. 6.
    Engelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. Wiley, New Jersey (2005)Google Scholar
  7. 7.
    Foster, I.: Designing and Building Parallel Programs, vol. 95. Addison-Wesley, Reading (1995)zbMATHGoogle Scholar
  8. 8.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Network, pp. 1942–1948. IEEE Press, Australia (1995)Google Scholar
  9. 9.
    Kirk, D.J., Hwu, W.: Programming Massively Parallel Processors. Morgan Kaufmann, San Francisco (2010)Google Scholar
  10. 10.
    Nedjah, N., Calazan, R.M., Mourelle, L.M.: Particle, dimension and cooperation-oriented PSO parallelization strategies for efficient high-dimension problem optimizations on graphics processing units. Comput. J. Sect. C: Comput. Intell. Mach. Learn. Data Anal. (2015). doi: 10.1093/comjnl/bxu153
  11. 11.
    Nedjah, N., Coelho, L.S., Mourelle, L.M.: Multi-Objective Swarm Intelligent Systems – Theory & Experiences. Springer, Berlin (2010)CrossRefGoogle Scholar
  12. 12.
    Papadakis, S.E., Bakrtzis, A.G.: A GPU accelerated PSO with application to economic dispatch problem. In: 16th International Conference on Intelligent System Application to Power Systems (ISAP), pp. 1–6. IEEE Press (2011)Google Scholar
  13. 13.
    Veronese, L., Krohling, R.A.: Swarm’s flight: accelerating the particles using C-CUDA. In: 11th IEEE Congress on Evolutionary Computation, pp. 3264–3270. IEEE Press, Trondheim (2009)Google Scholar
  14. 14.
    Zhou, Y., Tan, Y.: GPU-based parallel particle swarm optimization. In: 11th IEEE Congress on Evolutionary Computation (CEC 2009), pp. 1493–1500. IEEE Press, Trondheim (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nadia Nedjah
    • 1
    Email author
  • Rogério de Moraes Calazan
    • 2
  • Luiza de Macedo Mourelle
    • 3
  1. 1.Department of Electronics Engineering and TelecommunicationsState University of Rio de JaneiroRio de JaneiroBrazil
  2. 2.Center of ElectronicsCommunications and Information Technology, Brazilian NavyRio de JaneiroBrazil
  3. 3.Department of Systems Engineering and ComputationState University of Rio de JaneiroRio de JaneiroBrazil

Personalised recommendations