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A Parallel Adaptive PSO Algorithm with Non-iterative Electrostatic Repulsion and Social Dynamic Neighborhood

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Intelligent Systems Design and Applications (ISDA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 557))

Abstract

This paper presents a variation on the DEPSO-2S algorithm, called Parallel Adaptive PSO (PA2PSO). The goals of the PA2PSO algorithm are to find the value closest the global minimum of the function evaluated improving the location as well as the interaction of the particles by means of two important characteristics: non-iterative electrostatic repulsion and social dynamic neighborhood, and to reduce the response time with a parallel implementation. The PA2PSO achieves in most cases positive results in solving benchmark test functions (unimodal and multimodal functions) compared with nine outstanding PSO algorithms.

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References

  1. El Dor, A.: Perfectionnement des algorithmes d’optimisation pas essaim particulaire. Applications en segmentation d’images et en électronique. Ph.D. thesis, Université Paris-Est (2012)

    Google Scholar 

  2. El Dor, A., Clerc, M., Siarry, P.: Hybridization of differential evolution and particle swarm optimization in a new algorithm: DEPSO-2S. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) EC/SIDE -2012. LNCS, vol. 7269, pp. 57–65. Springer, Heidelberg (2012). doi:10.1007/978-3-642-29353-5_7

    Chapter  Google Scholar 

  3. El Dor, A., Lepagnot, J., Nakib, A., Siarry, P.: PSO-2S optimization algorithm for brain MRI segmentation. In: Pan, J.S., Krömer, P., Snášel, V. (eds.) Genetic and Evolutionary Computing. AISC, vol. 238. Springer, Cham, Heidelberg (2014). doi:10.1007/978-3-319-01796-9_2

    Google Scholar 

  4. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)

    Google Scholar 

  5. Ballerini, M., Cabibbo, N., Candelier, R., et al.: Interaction ruling animal collective behavior depends on topological rather than metric distance: evidence from a field study. In: Proceedings of National Academy of Sciences, pp. 1232–1237 (2008)

    Google Scholar 

  6. Martin, S., Girard, A.: Continuous-time consensus under persistent connectivity and slow divergence of reciprocal interaction weights. SIAM J. Control Optim. 51(3), 2568–2584 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  7. Nedjah, N., Calazan, R.D.M., de Macedo Mourelle, L., Wang, C.: Parallel implementations of the cooperative particle swarm optimization on many-core and multi-core architectures. Int. J. Parallel Prog. 44(6), 1173–1199 (2016)

    Article  Google Scholar 

  8. Poli, R.: Analysis of the publications on the applications of particle swarm optimisation. J. Artif. Evol. Appl. 1–10 (2008)

    Google Scholar 

  9. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf. Sci. 179(13), 2232–2248 (2009). Special Section on High Order Fuzzy Sets

    Article  MATH  Google Scholar 

  10. Shi, Y., Eberhart, R.: Empirical study of particle swarm optimization. In: IEEE International Conference on Evolutionary Computation, pp. 1945–1950 (1999)

    Google Scholar 

  11. Soto, D., Soto, W.: Adaptative particle swarm optimization algorithm with non-iterative electrostatic repulsion and social neighborhood. Actas de ingeniería 1, 55–60 (2015)

    Google Scholar 

  12. Vanneschi, L., Codecasa, D., Mauri, G.: An empirical study of parallel and distributed particle swarm optimization. In: de Vega, F.F., Pérez, J.I.H., Lanchares, J. (eds.) Parallel Architectures and Bioinspired Algorithms. SCI, vol. 415, pp. 125–150. Springer, Heidelberg (2012). doi:10.1007/978-3-642-28789-3_6

    Chapter  Google Scholar 

  13. de Vega, F.F., Pérez, J.I.H., Lanchares, J.: Parallel Architectures and Bioinspired Algorithms. Springer Publishing Company, Incorporated, Berlin (2014)

    Google Scholar 

  14. Venkata, R., Patel, V.: Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems. Int. J. Ind. Eng. Comput. 4(1), 29–50 (2013)

    Google Scholar 

  15. Wang, C., Liu, Y., Zhao, Y.: Application of dynamic neighborhood small population particle swarm optimization for reconfiguration of shipboard power system. Eng. Appl. Artif. Intell. 26(4), 1255–1262 (2013)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Antioquia University, Medellín, Colombia

    Daniel Soto

  2. School of Exact Sciences and Engineering, Sergio Arboleda University, Bogotá, Colombia

    Wilson Soto

Authors
  1. Daniel Soto
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  2. Wilson Soto
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Corresponding author

Correspondence to Daniel Soto .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  3. Polytechnic Institute of Porto, Felgueiras, Portugal

    Dorabela Gamboa

  4. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

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Soto, D., Soto, W. (2017). A Parallel Adaptive PSO Algorithm with Non-iterative Electrostatic Repulsion and Social Dynamic Neighborhood. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_56

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  • DOI: https://doi.org/10.1007/978-3-319-53480-0_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53479-4

  • Online ISBN: 978-3-319-53480-0

  • eBook Packages: EngineeringEngineering (R0)

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