Skip to main content
SpringerLink
Log in

Particle Swarm Optimization Algorithm for Dynamic Environments

  • Conference paper
  • First Online:
Advances in Artificial Intelligence and Soft Computing (MICAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9413))

Included in the following conference series:

Abstract

Particle Swarm Optimization (PSO) algorithm is considered as one of the crowd intelligence optimization algorithms. Dynamic optimization problems in which change(s) may happen over the time are harder to manage than static optimization problems. In this paper an algorithm based on PSO and memory for solving dynamic optimization problems has been proposed. The proposed algorithm uses the memory to store the aging best solutions and uses partitioning for preventing convergence in the population. The proposed approach has been tested on moving peaks benchmark (MPB). The MPB is a suitable problem for simulating dynamic optimization problems. The experimental results on the moving peaks benchmark show that the proposed algorithm is superior to several other well-known and state-of-the-art algorithms in dynamic environments.

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. Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of Congress on Evolutionary Computation, vol. 3. pp. 1875–1882 (1999)

    Google Scholar 

  2. Yang, S.: Associative memory scheme for genetic algorithms in dynamic environments. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 788–799. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  3. Yang, S., Yao, X.: Population-based incremental learning with associative memory for dynamic environments. IEEE Trans. Evol. Comput. 12(5), 542–561 (2008)

    Article  Google Scholar 

  4. Cobb, H.G., Grefenstette, J.J.: Genetic algorithms for tracking changing environments. In: Proceedings of 5th International Genetic Algorithms Conference, pp. 523–530 (1993)

    Google Scholar 

  5. Grefenstette, J.J.: Genetic algorithms for changing environments. In: Proceedings of 2nd International Conference Parallel Problem Solving from Nature, pp. 137–144 (1992)

    Google Scholar 

  6. Yang, S.: Genetic algorithms with memory and elitism-based immigrants in dynamic environment. Evol. Comput. 16(3), 385–416 (2008)

    Article  Google Scholar 

  7. Ramsey, C.L., Grefenstette, J.J.: Case-based initialization of genetic algorithms. In: Forrest, S. (ed.) Proceedings of the 5th International Conference on Genetic Algorithms. Morgan Kaufmann, pp. 84–91 (1993)

    Google Scholar 

  8. Louis, S.J., Xu, Z.: Genetic algorithms for open shop scheduling and re-scheduling. In: Cohen, M.E., Hudson, D.L. (eds) Proceedings of the 11th International Conference on Computers and their Applications (ISCA), pp. 99–102 (1996)

    Google Scholar 

  9. Yang, S., Tinos, R.: A hybrid immigrants scheme for genetic algorithms in dynamic environments. Int. J. Autom. Comput. 3(4), 243–254 (2007)

    Article  Google Scholar 

  10. Trojanowski, K., Michalewicz, Z.: Searching for optima in non-stationary environments. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1999). IEEE Press, pp. 1843–1850 (1999)

    Google Scholar 

  11. Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1999). IEEE Press, pp. 1875–1882 (1999)

    Google Scholar 

  12. Yang, S., Li, C.: A clustering particle swarm optimizer for dynamic optimization. In: Proceedings of Congress on Evolutionary Computation, pp. 439–446 (2009)

    Google Scholar 

  13. Blackwell, T., Branke, J., Li, X.: Particle swarms for dynamic optimization problems. Swarm Intelligence, pp. 193–217. Springer, Berlin (2008)

    Chapter  Google Scholar 

  14. Blackwell, T.M., Branke, J.: Multiswarms, exclusion, and anticon vergence in dynamic environments. IEEE Trans. Evol. Comput. 10(4), 459–472 (2006)

    Article  Google Scholar 

  15. Lung, R.I., Dumitrescu, D.: Evolutionary swarm cooperative optimization in dynamic environments. Nat. Comput. 9(1), 83–94 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  16. Lung, R.I., Dumitrescu, D.: A collaborative model for tracking optima in dynamic environments. In: Proceedings of Congress on Evolutionary Computation, pp. 564–567 (2007)

    Google Scholar 

  17. Li, X.: Adaptively choosing neighborhood bests using species in a particle swarm optimizer for multimodal function optimization. In: Proceedings of Genetic Evolutionary Computation Conference, pp. 105–116 (2004)

    Google Scholar 

  18. Yang, S., Li, C.: A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments. IEEE Trans. 16(4), 556–577 (2012)

    Google Scholar 

  19. Liu, L., Yang, S., Wang, D.: Particle swarm optimization with composite particles in dynamic environments. IEEE Trans. Syst. Man Cybern. B Cybern. 40(6), 1634–1648 (2010)

    Article  Google Scholar 

  20. Kamosi, M., Hashemi, A.B., Meybodi, M.R.: A hibernating multiswarm optimization algorithm for dynamic environments. In: Proceedings of World Congress on Nature and Biologically Inspired Computing, pp. 363–369 (2010)

    Google Scholar 

  21. Woldesenbet, Y.G., Yen, G.G.: Dynamic evolutionary algorithm with variable relocation. IEEE Trans. Evol. Comput. 13(3), 500–513 (2009)

    Article  Google Scholar 

  22. Yang, S., Li, C.: Fast multi-swarm optimization for dynamic optimization problems. Proc. Int. Conf. Nat. Comput. 7(3), 624–628 (2008)

    Google Scholar 

  23. Hashemi, A.B., Meybodi, M.R.: Cellular PSO: a PSO for dynamic environments. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds.) ISICA 2009. LNCS, vol. 5821, pp. 422–433. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  24. Wang, H., Yang, S., Ip, W.H., Wang, D.: A memetic particle swarm optimization algorithm for dyanamic multi modal optimization problems. Int. J. Syst. Sci. 43(7), 1268–1283 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  25. Blackwell, T., Branke, J., Li, X.: Particle swarms for dynamic optimization problems. In: Yang, S.S., Li, C. (eds.) A Clustering Particle Swarm Optimizer for Locating and Intelligence, pp. 193–217. Springer, Berlin (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran

    Sadrollah Sadeghi, Hamid Parvin & Farhad Rad

  2. Young Researchers and Elite Club, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani, Iran

    Hamid Parvin

Authors
  1. Sadrollah Sadeghi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamid Parvin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Farhad Rad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hamid Parvin .

Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, Centro de Investigación en Computación, Mexico City, Mexico

    Grigori Sidorov

  2. Facultad de ciencias, Universidad Autónoma Nacional, México, Distrito Federal, Mexico

    Sofía N. Galicia-Haro

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Sadeghi, S., Parvin, H., Rad, F. (2015). Particle Swarm Optimization Algorithm for Dynamic Environments. In: Sidorov, G., Galicia-Haro, S. (eds) Advances in Artificial Intelligence and Soft Computing. MICAI 2015. Lecture Notes in Computer Science(), vol 9413. Springer, Cham. https://doi.org/10.1007/978-3-319-27060-9_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27060-9_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27059-3

  • Online ISBN: 978-3-319-27060-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation