Skip to main content
SpringerLink
Log in

Searching Relevant Variable Subsets in Complex Systems Using K-Means PSO

  • Conference paper
  • First Online:
Artificial Life and Evolutionary Computation (WIVACE 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 830))

Included in the following conference series:

Abstract

The Relevance Index method has been shown to be effective in identifying Relevant Sets in complex systems, i.e., variable sub-sets that exhibit a coordinated behavior, along with a clear independence from the remaining variables. The need for computing the Relevance Index for each possible variable sub-set makes such a computation unfeasible, as the size of the system increases. Because of this, smart search methods are needed to analyze large-size systems using such an approach. Niching metaheuristics provide an effective solution to this problem, as they join search capabilities to good exploration properties, which allow them to explore different regions of the search space in parallel and converge onto several local/global minima.

In this paper, we describe the application of a niching metaheuristic, K-means PSO, to a set of complex systems of different size, comparing, when possible, its results with the ground truth represented by the results of an exhaustive search, while we rely on the analysis of a domain expert to assess the results of larger systems. In all cases, we also compare the results of K-means PSO to another metaheuristic, based on a niching genetic algorithm, that we had previously developed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. CUDA Toolkit. http://developer.nvidia.com/cuda-toolkit. Accessed 12 Mar 2018

  2. Atabay, H.A., Sheikhzadeh, M.J., Torshizi, M.: A clustering algorithm based on integration of K-means and PSO. In: 2016 1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC), pp. 59–63, March 2016

    Google Scholar 

  3. Bird, S., Li, X.: Adaptively choosing niching parameters in a PSO. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO 2006, pp. 3–10. ACM, New York (2006)

    Google Scholar 

  4. Bokhari, S.M.A., Basharat, I., Khan, S.A., Qureshi, A.W., Ahmed, B.: A framework for clustering dental patients’ records using unsupervised learning techniques. In: 2015 Science and Information Conference (SAI), pp. 386–394, July 2015

    Google Scholar 

  5. Brits, R., Engelbrecht, A., van den Bergh, F.: A niching particle swarm optimizer. In: 4th Asia-Pacific Conference on Simulated Evolution and Learning, pp. 692–696, January 2002

    Google Scholar 

  6. Brits, R., Engelbrecht, A.P., van den Bergh, F.: Solving systems of unconstrained equations using particle swarm optimization. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 3, p. 6, October 2002

    Google Scholar 

  7. Canale, S., Giorgio, A.D., Lisi, F., Panfili, M., Celsi, L.R., Suraci, V., Priscoli, F.D.: A future internet oriented user centric extended intelligent transportation system. In: 2016 24th Mediterranean Conference on Control and Automation (MED), pp. 1133–1139, June 2016

    Google Scholar 

  8. Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)

    Article  Google Scholar 

  9. Cover, T., Thomas, A.: Elements of Information Theory, 2nd edn. Wiley-Interscience, New York (2006)

    MATH  Google Scholar 

  10. Doreswamy, Salma, M.U.: PSO based fast K-means algorithm for feature selection from high dimensional medical data set. In: 2016 10th International Conference on Intelligent Systems and Control (ISCO), pp. 1–6, January 2016

    Google Scholar 

  11. Filisetti, A., Villani, M., Roli, A., Fiorucci, M., Poli, I., Serra, R.: On some properties of information theoretical measures for the study of complex systems. In: Pizzuti, C., Spezzano, G. (eds.) WIVACE 2014. CCIS, vol. 445, pp. 140–150. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12745-3_12

    Google Scholar 

  12. Gershenson, C., Fernandez, N.: Complexity and information: measuring emergence, self-organization, and homeostasis at multiple scales. Complexity 18(2), 29–44 (2012)

    Article  Google Scholar 

  13. Goudarzi, S., Hassan, W.H., Anisi, M.H., Soleymani, A., Sookhak, M., Khan, M.K., Hashim, A.H.A., Zareei, M.: ABC-PSO for vertical handover in heterogeneous wireless networks. Neurocomputing 256(Supplement C), 63–81 (2017). Fuzzy Neuro Theory and Technologies for Cloud Computing

    Article  Google Scholar 

  14. Kumar, G., Sarth, P.P., Ranjan, P., Kumar, S.: Satellite image clustering and optimization using K-means and PSO. In: 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), pp. 1–4, July 2016

    Google Scholar 

  15. Li, H., He, H., Wen, Y.: Dynamic particle swarm optimization and K-means clustering algorithm for image segmentation. Optik-Int. J. Light Electron Opt. 126(24), 4817–4822 (2015)

    Article  Google Scholar 

  16. Li, X.: Adaptively choosing neighbourhood bests using species in a particle swarm optimizer for multimodal function optimization. In: Deb, K. (ed.) GECCO 2004. LNCS, vol. 3102, pp. 105–116. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24854-5_10

    Chapter  Google Scholar 

  17. Liu, B., Li, Z.: Study on the automatic recognition of hidden defects based on Hilbert Huang transform and hybrid SVM-PSO model. In: 2017 Prognostics and System Health Management Conference (PHM-Harbin), pp. 1–7, July 2017

    Google Scholar 

  18. MacQueen, J., et al.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)

    Google Scholar 

  19. Nguyen, H.B., Xue, B., Andreae, P.: Mutual information for feature selection: estimation or counting? Evol. Intel. 9(3), 95–110 (2016)

    Article  Google Scholar 

  20. Parsopoulos, K.E., Plagianakos, V.P., Magoulas, G.D., Vrahatis, M.N.: Improving the particle swarm optimizer by function “stretching”. In: Hadjisavvas, N., Pardalos, P.M. (eds.) Advances in Convex Analysis and Global Optimization. Nonconvex Optimization and Its Applications, pp. 445–457. Springer, Boston (2001). https://doi.org/10.1007/978-1-4613-0279-7_28

    Chapter  Google Scholar 

  21. Passaro, A., Starita, A.: Particle swarm optimization for multimodal functions: a clustering approach. J. Artif. Evol. Appl. 2008, 15 p. (2008). https://doi.org/10.1155/2008/482032. Article ID 482032

  22. Poli, R., Kennedy, J., Blackwell, T.: Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)

    Article  Google Scholar 

  23. Sani, L., et al.: Efficient search of relevant structures in complex systems. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds.) AI*IA 2016. LNCS (LNAI), vol. 10037, pp. 35–48. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49130-1_4

    Chapter  Google Scholar 

  24. Schoeman, I.L.: Niching in particle swarm optimization. Ph.D. thesis, School of Engineering, University of Pretoria (2010)

    Google Scholar 

  25. Sun, Q., Wang, Y., Jiang, Y., Shao, L., Chen, D.: Fault diagnosis of SEPIC converters based on PSO-DBN and wavelet packet energy spectrum. In: 2017 Prognostics and System Health Management Conference (PHM-Harbin), pp. 1–7, July 2017

    Google Scholar 

  26. Tononi, G., McIntosh, A., Russel, D., Edelman, G.: Functional clustering: identifying strongly interactive brain regions in neuroimaging data. Neuroimage 7, 133–149 (1998)

    Article  Google Scholar 

  27. Vicari, E., et al.: GPU-based parallel search of relevant variable sets in complex systems. In: Rossi, F., Piotto, S., Concilio, S. (eds.) WIVACE 2016. CCIS, vol. 708, pp. 14–25. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57711-1_2

    Chapter  Google Scholar 

  28. Villani, M., Filisetti, A., Benedettini, S., Roli, A., Lane, D., Serra, R.: The detection of intermediate level emergent structures and patterns. In: Liò, P., Miglino, O., Nicosia, G., Nolfi, S., Pavone, M. (eds.) Proceedings of ECAL 2013, the 12th European Conference on Artificial Life. MIT Press (2013)

    Google Scholar 

  29. Villani, M., Roli, A., Filisetti, A., Fiorucci, M., Poli, I., Serra, R.: The search for candidate relevant subsets of variables in complex systems. Artif. Life 21(4), 412–431 (2015)

    Article  Google Scholar 

  30. Will, A., Bustos, J., Bocco, M., Gotay, J., Lamelas, C.: On the use of niching genetic algorithms for variable selection in solar radiation estimation. Renew. Energy 50, 168–176 (2013)

    Article  Google Scholar 

  31. Xue, B., Zhang, M., Browne, W.N., Yao, X.: A survey on evolutionary computation approaches to feature selection. IEEE Trans. Evol. Comput. 20(4), 606–626 (2016)

    Article  Google Scholar 

  32. Yannibelli, V., Amandi, A.: A deterministic crowding evolutionary algorithm to form learning teams in a collaborative learning context. Expert Syst. Appl. 39(10), 8584–8592 (2012)

    Article  Google Scholar 

Download references

Acknowledgments

The work of Michele Amoretti was supported by the University of Parma Research Fund - FIL 2016 - Project “NEXTALGO: Efficient Algorithms for Next-Generation Distributed Systems”.

The authors would like to thank Andrea Roli, Roberto Serra, and Marco Villani for the enlightening discussions and comments on this work.

Author information

Authors and Affiliations

  1. Dip. di Ingegneria e Architettura, Università di Parma, Parma, Italy

    Gianluigi Silvestri, Laura Sani, Michele Amoretti, Riccardo Pecori, Monica Mordonini & Stefano Cagnoni

  2. SMARTEST Research Centre, Università eCAMPUS, Novedrate, CO, Italy

    Riccardo Pecori

  3. CAMLIN Technologies Italy, Parma, Italy

    Emilio Vicari

Authors
  1. Gianluigi Silvestri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laura Sani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michele Amoretti
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Riccardo Pecori
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Emilio Vicari
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Monica Mordonini
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Stefano Cagnoni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stefano Cagnoni .

Editor information

Editors and Affiliations

  1. Ca’ Foscari University of Venice, Venice, Italy

    Marcello Pelillo

  2. Ca’ Foscari University of Venice, Venice, Italy

    Irene Poli

  3. University of Bologna, Cesena, Italy

    Andrea Roli

  4. University of Modena and Reggio Emilia, Modena, Italy

    Roberto Serra

  5. Ca’ Foscari University of Venice, Venice, Italy

    Debora Slanzi

  6. University of Modena and Reggio Emilia, Modena, Italy

    Marco Villani

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Silvestri, G. et al. (2018). Searching Relevant Variable Subsets in Complex Systems Using K-Means PSO. In: Pelillo, M., Poli, I., Roli, A., Serra, R., Slanzi, D., Villani, M. (eds) Artificial Life and Evolutionary Computation. WIVACE 2017. Communications in Computer and Information Science, vol 830. Springer, Cham. https://doi.org/10.1007/978-3-319-78658-2_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-78658-2_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-78657-5

  • Online ISBN: 978-3-319-78658-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation