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A Hybrid Particle Swarm Optimization Algorithm Based on Migration Mechanism

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Intelligence Science and Big Data Engineering (IScIDE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10559))

Abstract

Standard Particle Swarm Optimization 2011 is the latest version of standard PSO algorithm, with an adaptive random topology and rotational invariance. However, it needs to be improved on non-separable and asymmetrical problems. Migration mechanism is inspired by biogeography-based optimization, which is insensitive to dense swarms, and could effectively develop the local solution space. In this study, in order to avoid the algorithm has been in a local optimal state, when the optimal value of the algorithm does not improve within the threshold value, the introduction of migration mechanism used to jump out of that state. In addition, topological migration is used in the migration mechanism, in order to more effectively share the solution features and improve the search capabilities of the algorithm. Finally, six different types of benchmark functions are selected to compare the proposed algorithm with the above two algorithms, which verifies the effectiveness of the proposed algorithm.

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References

  1. Kennedy, J., Eberhart, R.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Australia, pp. 39–43. IEEE (1995)

    Google Scholar 

  2. Heppner, F., Grenander, U.: The Ubiquity of Chaos: A Stochastic Nonlinear Model for Coordinate Bird Flocks. AAAS Publications, American (1990)

    Google Scholar 

  3. Eberhart, R.C., Shi, Y.: Comparison between genetic algorithms and particle swarm optimization. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds.) EP 1998. LNCS, vol. 1447, pp. 611–616. Springer, Heidelberg (1998). doi:10.1007/BFb0040812

    Chapter  Google Scholar 

  4. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Evolutionary Computation Proceedings, Anchorage, pp. 69–73. IEEE (1998)

    Google Scholar 

  5. Clerc, M.: The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: Evolutionary Computation, Washington, pp. 1951–1957. IEEE (1999)

    Google Scholar 

  6. Kennedy, J.: Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Evolutionary Computation, Washington, pp. 1931—1938. IEEE (1999)

    Google Scholar 

  7. Parsopoulos, K.E., Plagianakos, V.P., Magoulas, G.D., Vrahatis, M.N.: Stretching technique for obtaining global minimizers through particle swarm optimization. In: Proceeding of the Particle Swarm Optimization Workshop, PSOW, Indianapolis, pp. 22–29. (2001)

    Google Scholar 

  8. Liu, Q., Han, F.: A hybrid attractive and repulsive particle swarm optimization based on gradient search. In: Huang, D.-S., Jo, K.-H., Zhou, Y.-Q., Han, K. (eds.) ICIC 2013. LNCS, vol. 7996, pp. 155–162. Springer, Heidelberg (2013). doi:10.1007/978-3-642-39482-9_18

    Chapter  Google Scholar 

  9. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: Computational Cybemetics and Simulation, Orlando, pp. 4104–4108. IEEE (1997)

    Google Scholar 

  10. Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: Swarm Intelligence Symposium, Honolulu, pp. 120—127. IEEE (2007)

    Google Scholar 

  11. Clerc, M.: Beyond Standard Particle Swarm Optimisation. IJSIR 1(4), 46–61 (2010)

    Google Scholar 

  12. Zambrano-Bigiarini, M., Clerc, M., Rojas, R.: Standard particle swarm optimisation 2011 at CEC-2013: a baseline for future PSO improvements. In: 2013 IEEE Congress on Evolutionary Computation (CEC), Cancun, Mexico, pp. 2337–2344. IEEE (2013)

    Google Scholar 

  13. Bonyadi, M.R., Michalewicz, Z.: SPSO 2011: analysis of stability; local convergence; and rotation sensitivity. In: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, Vancouver, pp. 9–16. ACM (2014)

    Google Scholar 

  14. Bonyadi, M.R., Michalewicz, Z.: Analysis of stability, local convergence, and transformation sensitivity of a variant of particle swarm optimization algorithm. IEEE Trans. Evol. Comput. 20(3), 370–385 (2016)

    Article  Google Scholar 

  15. Ugolotti, R., Cagnoni, S.: Automatic tuning of standard PSO versions. In: Proceeding of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 1501–1502. ACM, New York (2015)

    Google Scholar 

  16. Kamalapur, S.M., Patil, V.H.: Impact of acceleration coefficients and random neighborhood topology modification threshold on SPSO. In: 2015 International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, pp. 437–441. IEEE (2015)

    Google Scholar 

  17. Hariya, Y., Shindo, T., Jin’No, K.: An improved rotationally invariant PSO: a modified standard PSO-2011. In: 2016 IEEE Congress on Evolutionary Computation (CEC 2016), Vancouver, BC, Canada, pp. 1839–1844. IEEE (2016)

    Google Scholar 

  18. Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)

    Article  Google Scholar 

  19. Cheng, G., Lv, C., Yan, S., Xu, L.: A novel hybrid optimization algorithm combined with BBO and PSO. In: 2016 Chinese Control and Decision Conference, China, pp. 1198–1202. IEEE (2016)

    Google Scholar 

  20. Spears, W.M., Green, D.T., Spears, D.F.: Biases in particle swarm optimization. Int. J. Swarm Intell. Res. 1(2), 34–57 (2010)

    Article  Google Scholar 

  21. Clerc, M.: Back to random topology. Relatório Técnico (2007)

    Google Scholar 

  22. Ma, H., Dan, S.: Blended biogeography-based optimization for constrained optimization. Eng. Appl. Artif. Intell. 24(3), 517–525 (2011)

    Article  Google Scholar 

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 61572241 and 61271385), the Foundation of the Peak of Six Talents of Jiangsu Province (No. 2015-DZXX-024), and the Fifth “333 High Level Talented Person Cultivating Project” of Jiangsu Province.

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Authors and Affiliations

  1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu, China

    Ning Lai & Fei Han

Authors
  1. Ning Lai
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  2. Fei Han
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Corresponding author

Correspondence to Ning Lai .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Yi Sun

  2. Dalian University of Technology, Dalian, China

    Huchuan Lu

  3. Dalian University of Technology, Dalian, China

    Lihe Zhang

  4. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

  5. Beijing Institute of Technology, Beijing, China

    Hua Huang

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Lai, N., Han, F. (2017). A Hybrid Particle Swarm Optimization Algorithm Based on Migration Mechanism. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_8

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  • DOI: https://doi.org/10.1007/978-3-319-67777-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67776-7

  • Online ISBN: 978-3-319-67777-4

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