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Empirical Assessment of Human Learning Principles Inspired PSO Algorithms on Continuous Black-Box Optimization Testbed

  • M. R. TanweerEmail author
  • Abdullah Al-Dujaili
  • S. Suresh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9873)

Abstract

This paper benchmarks the performance of one of the recent research directions in the performance improvement of particle swarm optimization algorithm; human learning principles inspired PSO variants. This article discusses and provides performance comparison of nine different PSO variants. The Comparing Continuous Optimizers (COCO) methodology has been adopted in comparing these variants on the noiseless BBOB testbed, providing useful insight regarding their relative efficiency and effectiveness. This study provides the research community a comprehensive account of suitability of a PSO variant in solving selective class of problems under different budget settings. Further, certain rectifications/extensions have also been suggested for the selected PSO variants for possible performance enhancement. Overall, it has been observed that SL-PSO and MePSO are most suited for expensive and moderate budget settings respectively. Further, iSRPSO and TPLPSO have provided better solutions under cheap budget settings where iSRPSO has shown robust behaviour (better solutions over dimensions). We hope this paper would mark a milestone in assessing the human learning principles inspired PSO algorithms and used as a baseline for performance comparison.

Keywords

PSO Human learning principles inspired PSO variants COCO methodology Black-box optimization 

Notes

Acknowledgement

The authors wish to extend their thanks to the ATMRI:2014-R8, Singapore, for providing financial support to conduct this study.

References

  1. 1.
    Arya, M., Deep, K., Bansal, J.C.: A nature inspired adaptive inertia weight in particle swarm optimisation. Int. J. AI Soft Comput. 4(2–3), 228–248 (2014)Google Scholar
  2. 2.
    Cheng, R., Jin, Y.: A social learning particle swarm optimization algorithm for scalable optimization. Inf. Sci. 291, 43–60 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Cheng, R., Jin, Y.: A competitive swarm optimizer for large scale optimization. IEEE Trans. Cybern. 45(2), 191–204 (2015)CrossRefGoogle Scholar
  4. 4.
    Epitropakis, M., Plagianakos, V., Vrahatis, M.: Evolving cognitive and social experience in particle swarm optimization through differential evolution: a hybrid approach. Inf. Sci. 216(1), 50–92 (2012)CrossRefGoogle Scholar
  5. 5.
    Eslami, M., Shareef, H., Khajehzadeh, M., Mohamed, A.: A survey of the state of the art in particle swarm optimization. Res. J. Appl. Sci. Eng. Technol. 4(9), 1181–1197 (2012)Google Scholar
  6. 6.
    Finck, S., Hansen, N., Ros, R., Auger, A.: Real-parameter black-box optimization benchmarking 2009: presentation of the noiseless functions. Technical Report 2009/20, Research Center PPE (2009). Updated, February 2010Google Scholar
  7. 7.
    Hansen, N., Auger, A., Finck, S., Ros, R.: Real-parameter black-box optimization benchmarking 2012: experimental setup. Technical report, INRIA (2012)Google Scholar
  8. 8.
    Hansen, N., Finck, S., Ros, R., Auger, A.: Real-parameter black-box optimization benchmarking 2009: noiseless functions definitions. Technical report RR-6829, INRIA (2009). Updated February 2010Google Scholar
  9. 9.
    Huang, H., Qin, H., Hao, Z., Lim, A.: Example-based learning particle swarm optimization for continuous optimization. Inf. Sci. 182(1), 125–138 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  11. 11.
    Liang, J., Qin, A., Suganthan, P., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)CrossRefGoogle Scholar
  12. 12.
    Lim, W., Isa, N.: Teaching and peer-learning particle swarm optimization. Appl. Soft Comput. 18, 39–58 (2014)CrossRefGoogle Scholar
  13. 13.
    Lynn, N., Suganthan, P.: Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm Evol. Comput. 24, 11–24 (2015)CrossRefGoogle Scholar
  14. 14.
    Nelson, T., Narens, L.: Metamemory: a theoretical framework and new findings. Psychol. Learn. Motiv. 26, 125–141 (1990)CrossRefGoogle Scholar
  15. 15.
    Poli, R.: Analysis of the publications on the applications of particle swarm optimization. Artif. Evol. Appl. 28, 1–10 (2008)Google Scholar
  16. 16.
    Price, K.: Differential evolution vs. the functions of the second ICEO. In: Proceedings of the IEEE International CEC, pp. 153–157 (1997)Google Scholar
  17. 17.
    Shi, Y.: Brain storm optimization algorithm. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011. LNCS, vol. 6728, pp. 303–309. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-21515-5_36 CrossRefGoogle Scholar
  18. 18.
    Sun, S., Li, J.: A two-swarm cooperative particle swarms optimization. Swarm Evol. Comput. 15, 1–18 (2014)CrossRefGoogle Scholar
  19. 19.
    Suresh, S., Sujit, P., Rao, A.: Particle swarm optimization approach for multi-objective composite box-beam design. Compos. Struct. 81(4), 598–605 (2007)CrossRefGoogle Scholar
  20. 20.
    Tanweer, M.R., Suresh, S., Sundararajan, N.: Human meta-cognition inspired collaborative search algorithm for optimization. In: IEEE MFI, pp. 1–6 (2014)Google Scholar
  21. 21.
    Tanweer, M.R., Suresh, S., Sundararajan, N.: Self regulating particle swarm optimization algorithm. Inf. Sci. 294, 182–202 (2014)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Tanweer, M.R., Suresh, S., Sundararajan, N.: Dynamic mentoring and self-regulation based particle swarm optimization algorithm for solving complex real-world optimization problems. Inf. Sci. 326, 1–24 (2015)CrossRefGoogle Scholar
  23. 23.
    Tanweer, M.R., Suresh, S., Sundararajan, N.: Improved SRPSO algorithm for solving CEC 2015 computationally expensive numerical optimization problems. In: IEEE CEC, pp. 1943–1949 (2015)Google Scholar
  24. 24.
    Tanweer, M.R., Suresh, S., Sundararajan, N.: Mentoring based particle swarm optimization algorithm for faster convergence. In: IEEE CEC, pp. 196–203 (2015)Google Scholar
  25. 25.
    Wang, H., Qiao, Z., Xia, C., Li, L.: Self-regulating and self-evolving particle swarm optimizer. Eng. Opt. 47(1), 129–147 (2015)CrossRefGoogle Scholar
  26. 26.
    Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)CrossRefGoogle Scholar
  27. 27.
    Zhang, Y., Wang, S., Ji, G.: A comprehensive survey on particle swarm optimization algorithm and its applications. Math. Prob. Eng. 501, 931256 (2015)MathSciNetGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • M. R. Tanweer
    • 1
    Email author
  • Abdullah Al-Dujaili
    • 1
  • S. Suresh
    • 1
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingaporeSingapore

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