Skip to main content
SpringerLink
Log in
Book cover

Combining Soft Computing and Statistical Methods in Data Analysis pp 289–296Cite as

Particle Swarm Optimization and Inverse Problems

  • Conference paper

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 77))

Abstract

In this paper we present a powerful set of Particle Swarm optimizers for inverse modeling. Their design is based on the interpretation of the swarm dynamics as a stochastic damped mass-spring system. All the PSO optimizers have very different exploitation and exploration capabilities. Their convergence can be related to the stability of their first and second order moments of the particle trajectories. Based on these results we present their corresponding cloud algorithms where each particle in the swarm has different inertia (damping) and acceleration (rigidity) constants. These algorithms show a very good balance between exploration and exploitation and their use avoids the tuning of the PSO parameters. These algorithms have been successfully applied to environmental geophysics and petroleum reservoir engineering where the combined use of model reduction techniques allow posterior sampling in high dimensional spaces.

This is a preview of subscription content, 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   259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   329.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Birge, B.: PSOt - a particle swarm optimization toolbox for use with Matlab. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium, SIS 2003, Indianapolis, Indiana, USA, pp. 182–186 (2003)

    Google Scholar 

  2. Carlisle, A., Dozier, G.: An off-the-shelf PSO. In: Proceedings of the Particle Swarm Optimization Workshop, Indianapolis, Indiana, USA, pp. 1–6 (2001)

    Google Scholar 

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

    Article  Google Scholar 

  4. Fernández Martínez, J.L., García-Gonzalo, E.: The generalized PSO: a new door to PSO evolution. J. Artificial Evol. Appl. ID 861275 (2008), doi:10.1155/2008/861275

    Google Scholar 

  5. Fernández-Martínez, J.L., García-Gonzalo, E.: The PSO family: deduction, stochastic analysis and comparison. Swarm Intell. 3, 245–273 (2009)

    Article  Google Scholar 

  6. Fernández-Martínez, J.L., García-Gonzalo, E.: Stochastic stability analysis of the linear continuous and discrete PSO models. Technical Report, Department of Mathematics, University of Oviedo, Spain (2009)

    Google Scholar 

  7. Fernández-Martínez, J.L., García-Gonzalo, E.: What makes Particle Swarm Optimization a very interesting and powerful algorithm? In: Handbook of Swarm Intelligence – Concepts, Principles and Applications Series on Adaptation, Learning, and Optimization. Springer, Heidelberg (to appear, 2010)

    Google Scholar 

  8. Fernández-Martínez, J.L., García-Gonzalo, E., Fernández-Alvarez, J.P.: Theoretical analysis of particle swarm trajectories through a mechanical analogy. Int. J. Comput. Intell. Res. 4, 93–104 (2008)

    MathSciNet  Google Scholar 

  9. Fernández-Martínez, J.L., García-Gonzalo, E., Fernández-Álvarez, J.P., Kuzma, H.A., Menéndez-Pérez, C.O.: PSO: A Powerful Algorithm to Solve Geophysical Inverse Problems. Application to a 1D-DC Resistivity Case. J. Appl. Geophys. (accepted for publication, 2010)

    Google Scholar 

  10. Fernández-Martínez, J.L., García-Gonzalo, E., Fernández-Muñiz, Z., Mukerji, T.: How to design a powerful family of Particle Swarm Optimizers for inverse modeling. New Trends on Bio-inspired Computation. Trans. Inst. Meas. Contr. (accepted for publication, 2010)

    Google Scholar 

  11. Fernández-Martínez, J.L., García-Gonzalo, E., Naudet, V.: Particle Swarm Optimization applied to the solving and appraisal of the Streaming Potential inverse problem. Geophys, Hydrogeophysics Special Issue (accepted for publication, 2010)

    Google Scholar 

  12. Fernández-Martínez, J.L., Mukerji, T., García-Gonzalo, E.: Particle Swarm Optimization in high dimensional spaces. In: Proceedings of the Seventh International Conference on Swarm Intelligence, ANTS 2010, Bruxelles, Belgium (2010)

    Google Scholar 

  13. García-Gonzalo, E., Fernández-Martínez, J.L.: Design of a simple and powerful Particle Swarm optimizer. In: Proceedings of the International Conference on Computational and Mathematical Methods in Science and Engineering, CMMSE 2009, Gijón, Spain (2009)

    Google Scholar 

  14. García-Gonzalo, E., Fernández-Martínez, J.L.: The PP-GPSO and RR-GPSO. Technical Report. Department of Mathematics. University of Oviedo, Spain (submitted for publication, 2010)

    Google Scholar 

  15. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, ICNN 1995, Perth, WA, Australia, pp. 1942–1948 (1995)

    Google Scholar 

  16. Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf. Processing Lett. 85, 317–325 (2003)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics, University of Oviedo, Oviedo, Spain

    Esperanza García-Gonzalo & Juan Luis Fernández-Martínez

  2. Energy Resources Department, Stanford University, Palo Alto, California, USA

    Juan Luis Fernández-Martínez

  3. Department of Civil and Environmental Engineering, University of California at Berkeley, Berkeley, USA

    Esperanza García-Gonzalo

Authors
  1. Esperanza García-Gonzalo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juan Luis Fernández-Martínez
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Research Unit on Intelligent Data Analysis and Graphical Models, European Centre for Soft Computing Edificio Científico-Tecnológico. 3a Planta, C/ Gonzalo Gutiérrez Quirós s/n, 33600, Mieres, Spain

    Christian Borgelt , Gil González-Rodríguez  & Wolfgang Trutschnig ,  & 

  2. Departamento de Estadística e I.O. y D.M., Universidad de Oviedo, Facultad de Ciencias, C/ Calvo Sotelo s/n, 33007, Oviedo, Spain

    María Asunción Lubiano

  3. Departamento de Estadística e I.O. yD.M., Universidad de Oviedo, Facultad de Ciencias, C/ Calvo Sotelo s/n, 33007, Oviedo, Spain

    María Ángeles Gil

  4. Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447, Warsaw, Poland

    Przemysław Grzegorzewski

  5. Systems Research Institute, Polish Academy of Science, Newelska 6, 01-447, Warsaw, Poland

    Olgierd Hryniewicz

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

García-Gonzalo, E., Fernández-Martínez, J.L. (2010). Particle Swarm Optimization and Inverse Problems. In: Borgelt, C., et al. Combining Soft Computing and Statistical Methods in Data Analysis. Advances in Intelligent and Soft Computing, vol 77. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14746-3_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-14746-3_36

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14745-6

  • Online ISBN: 978-3-642-14746-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation