Skip to main content
SpringerLink
Log in

Underwater Glider Path Planning and Population Size Reduction in Differential Evolution

  • Conference paper
  • First Online:
Computer Aided Systems Theory – EUROCAST 2015 (EUROCAST 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9520))

Included in the following conference series:

Abstract

This paper presents an approach to underwater glider path planning (UGPP), where the population size reduction mechanism is introduced into the differential evolution (DE) meta-heuristic and two types of DE strategies (DE/best and DE/rand) are applied interchangeably. The newly proposed DE instance algorithms using population size reduction on the best and rand DE strategies are assessed and compared on 12 test scenarios using the proposed approach. A Bonferroni-Dunns statistical hypothesis testing is conducted to confirm out-performance of the favoured DE/best strategy over the DE/rand strategy for the 12 UGGP scenarios utilized. The analysis suggests that the approach can benefit from gradually reducing the population size and also tuning the DE parameters. Thereby, this contributes to extend the operational capabilities of the glider vehicle and to improve its value as a marine sensor, facilitating the implementation of flexible sampling schemes.

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. Alvarez, A., Caiti, A., Onken, R.: Evolutionary path planning for autonomous underwater vehicles in a variable ocean. IEEE J. Oceanic Eng. 29(2), 418–429 (2004)

    Article  Google Scholar 

  2. Bošković, B., Brest, J., Zamuda, A., Greiner, S., Žumer, V.: History mechanism supported differential evolution for chess evaluation function tuning. Soft Comput. Fusion Found. Method. Appl. 15(4), 667–682 (2011)

    Google Scholar 

  3. Brest, J., Greiner, S., Bošković, B., Mernik, M., Žumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)

    Article  Google Scholar 

  4. Brest, J., Korošec, P., Šilc, J., Zamuda, A., Bošković, B., Maučec, M.S.: Differential evolution and differential ant-stigmergy on dynamic optimisation problems. Int. J. Syst. Sci. 44(4), 663–679 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  5. Brest, J., Maučec, M.S.: Population size reduction for the differential evolution algorithm. Appl. Intell. 29(3), 228–247 (2008)

    Article  Google Scholar 

  6. Cabrera-Gámez, J., Isern-González, J., Hernández-Sosa, D., Domínguez-Brito, A.C., Fernández-Perdomo, E.: Optimization-Based Weather Routing for Sailboats. In: Sauze, C., Finnis, J. (eds.) Robotic Sailing 2012, pp. 23–34. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  7. Darwin, C.: On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for Life. John Murray, London (1859)

    Book  Google Scholar 

  8. Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011)

    Article  Google Scholar 

  9. Davis, R.E., Leonard, N.E., Fratantoni, D.M.: Routing strategies for underwater gliders. Deep Sea Res. Part II 56(3), 173–187 (2009)

    Article  Google Scholar 

  10. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Natural Computing Series. Springer, Heidelberg (2003)

    Book  MATH  Google Scholar 

  11. Garau, B., Alvarez, A., Oliver, G.: Path planning of autonomous underwater vehicles in current fields with complex spatial variability: an A* approach. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, ICRA 2005, pp. 194–198. IEEE (2005)

    Google Scholar 

  12. Hátún, H., Eriksen, C.C., Rhines, P.B.: Buoyant eddies entering the Labrador Sea observed with gliders and altimetry. J. Phys. Oceanogr. 37(12), 2838–2854 (2007)

    Article  Google Scholar 

  13. Hernández Sosa, D.J., Smith, R., Fernández-Perdomo, E., Isern-González, J., Cabrera, J., Domínguez-Brito, A.C., Prieto-Marañón, V.: Glider path-planning for optimal sampling of mesoscale eddies. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2013, Part II. LNCS, vol. 8112, pp. 321–325. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  14. Inanc, T., Shadden, S.C., Marsden, J.E.: Optimal trajectory generation in ocean flows. In: Proceedings of the American Control Conference, Portland, OR, USA, pp. 674–679 (2004)

    Google Scholar 

  15. Joshi, R., Sanderson, A.: Minimal representation multisensor fusion using differential evolution. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 29(1), 1083–4427 (1999)

    Article  Google Scholar 

  16. Lagarias, J.C., Reeds, J.A., Wright, M.H., Wright, P.E.: Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM J. Optim. 9(1), 112–147 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  17. Leonard, N.E., Paley, D.A., Davis, R.E., Fratantoni, D.M., Lekien, F., Zhang, F.: Coordinated control of an underwater glider fleet in an adaptive ocean sampling field experiment in Monterey Bay. J. Field Rob. 27(6), 718–740 (2010)

    Article  Google Scholar 

  18. Moura, A., Rijo, R., Silva, P., Crespo, S.: A multi-objective genetic algorithm applied to autonomous underwater vehicles for sewage outfall plume dispersion observations. Appl. Soft Comput. 10(4), 1119–1126 (2010)

    Article  Google Scholar 

  19. Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Natural Computing Series. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  20. Rudnick, D.L., Davis, R.E., Eriksen, C.C., Fratantoni, D.M., Perry, M.J.: Underwater gliders for ocean research. Marine Tech. Soc. J. 38(2), 73–84 (2004)

    Article  Google Scholar 

  21. Smith, R.N., Chao, Y., Li, P.P., Caron, D.A., Jones, B.H., Sukhatme, G.S.: Planning and implementing trajectories for autonomous underwater vehicles to track evolving ocean processes based on predictions from a regional ocean model. Int. J. Rob. Res. 29(12), 1475–1497 (2010)

    Article  Google Scholar 

  22. Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  23. Zaharie, D.: Influence of crossover on the behavior of differential evolution algorithms. Appl. Soft Comput. 9(3), 1126–1138 (2009)

    Article  Google Scholar 

  24. Zamuda, A., Brest, J., Mezura-Montes, E.: Structured population size reduction differential evolution with multiple mutation strategies on CEC 2013 real parameter optimization. In: 2013 IEEE Conference on Evolutionary Computation, vol. 1, pp. 1925–1931, 20–23 June 2013

    Google Scholar 

  25. Zamuda, A., Sosa, J.D.H.: Underwater glider path planning and population reduction in differential evolution. In: Fifteenth International Conference on Computer Aided Systems Theory, Museo Elder de la Ciencia y la Tecnologa, Las Palmas de Gran Canaria, Canary Islands, Spain, 8–13 February 2015, pp. 274–275 (2015)

    Google Scholar 

  26. Zamuda, A., Sosa, J.D.H.: Differential evolution and underwater glider path planning applied to the short-term opportunistic sampling of dynamic mesoscale ocean structures. Appl. Soft Comput. 24, 95–108 (2014)

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially funded by the Slovenian Research Agency under project P2-0041 and the Canary Island government and FEDER funds under project 2010/62. The codes in Matlab for extending the optimization algorithms utilized are provided by Qingfu Zhang at http://dces.essex.ac.uk/staff/qzhang/code/.

Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ul. 17, 2000, Maribor, Slovenia

    Aleš Zamuda

  2. Institute of Intelligent Systems and Numerical Applications in Engineering, University of Las Palmas de Gran Canaria, Campus de Tafira, 35017, Las Palmas de Gran Canaria, Spain

    José Daniel Hernández-Sosa

Authors
  1. Aleš Zamuda
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José Daniel Hernández-Sosa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aleš Zamuda .

Editor information

Editors and Affiliations

  1. de Gran Canaria, Universidad de las Palmas de Gran Canari, Las Palmas de Gran Canaria, Spain

    Roberto Moreno-Díaz

  2. Johannes Kepler University Linz, Linz, Austria

    Franz Pichler

  3. de Gran Canaria, Universidad de las Palmas de Gran Canari, Las Palmas de Gran Canaria, Spain

    Alexis Quesada-Arencibia

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Zamuda, A., Hernández-Sosa, J.D. (2015). Underwater Glider Path Planning and Population Size Reduction in Differential Evolution. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2015. EUROCAST 2015. Lecture Notes in Computer Science(), vol 9520. Springer, Cham. https://doi.org/10.1007/978-3-319-27340-2_104

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27340-2_104

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27339-6

  • Online ISBN: 978-3-319-27340-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation