Skip to main content
Log in

A cooperative strategy for solving dynamic optimization problems

Memetic Computing Aims and scope Submit manuscript

Abstract

Optimization in dynamic environments is a very active and important area which tackles problems that change with time (as most real-world problems do). In this paper we present a new centralized cooperative strategy based on trajectory methods (tabu search) for solving Dynamic Optimization Problems (DOPs). Two additional methods are included for comparison purposes. The first method is a Particle Swarm Optimization variant with multiple swarms and different types of particles where there exists an implicit cooperation within each swarm and competition among different swarms. The second method is an explicit decentralized cooperation scheme where multiple agents cooperate to improve a grid of solutions. The main goals are: firstly, to assess the possibilities of trajectory methods in the context of DOPs, where populational methods have traditionally been the recommended option; and secondly, to draw attention on explicitly including cooperation schemes in methods for DOPs. The results show how the proposed strategy can consistently outperform the results of the two other methods.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Battiti R, Brunato M, Mascia F (2008) Reactive search and intelligent optimization. Operations research/computer science interfaces, vol 45. Springer, New York

    Google Scholar 

  2. Blackwell T, Branke J (2006) Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Trans Evol Comput 10(4): 459–472

    Article  Google Scholar 

  3. Branke J (1999) Memory enhanced evolutionary algorithms for changing optimization problems. In: Congress on evolutionary computation CEC99, IEEE, pp 1875–1882

  4. Branke J (2001) Evolutionary optimization in dynamic environments. Kluwer, Norwell

    Google Scholar 

  5. Branke J, Schmeck H (2003) Designing evolutionary algorithms for dynamic optimization problems. Advances in evolutionary computing: theory and applications, pp 239–262

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

    Article  Google Scholar 

  7. Cruz C, Pelta D (2009) Soft computing and cooperative strategies for optimization. Appl Soft Comput 9(1): 30–38

    Article  Google Scholar 

  8. Eberhart RC, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 congress on evolutionary computation, vol 1, pp 84–88

  9. Ferber J (1999) Multi-agent systems: an introduction to distributed artificial intelligence. Addison-Wesley Longman, Boston

    Google Scholar 

  10. Franzè F, Speciale N (2001) A tabu-search-based algorithm for continuous multiminima problems. Int J Numer Methods Eng 50: 665–680

    Article  MATH  Google Scholar 

  11. Hedar A, Fukushima M (2004) Heuristic pattern search and its hybridization with simulated annealing for nonlinear global optimization. Optim Methods Softw 19: 291–308

    Article  MATH  MathSciNet  Google Scholar 

  12. Hedar AR, Fukushima M (2006) Tabu search directed by direct search methods for nonlinear global optimization. Eur J Oper Res 170: 329–349

    Article  MATH  MathSciNet  Google Scholar 

  13. Masegosa AD, Mascia F, Pelta D, Brunato M (2009) Cooperative strategies and reactive search: a hybrid model proposal. In: Learning and intelligent optimization. Lecture notes in computer science, vol 5851. Springer, Berlin, pp 206–220

  14. Pelta D, Sancho-Royo A, Cruz C, Verdegay JL (2006) Using memory and fuzzy rules in a co-operative multi-thread strategy for optimization. Inform Sci 176(13): 1849–1868

    Article  Google Scholar 

  15. Pelta D, Cruz C, Gonzalez JR (2009) A study on diversity and cooperation in a multiagent strategy for dynamic optimization problems. Int J Intell Syst 24: 844–861

    Article  MATH  Google Scholar 

  16. Pelta D, Cruz C, Verdegay JL (2009) Simple control rules in a cooperative system for dynamic optimisation problems. Int J Gen Syst 38(7): 701–717

    Article  MATH  Google Scholar 

  17. Richter H, Yang S (2009) Learning behavior in abstract memory schemes for dynamic optimization problems. Soft Comput Fusion Found Methodol Appl 13(12): 1163–1173

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Juan R. González.

Rights and permissions

Reprints and permissions

About this article

Cite this article

González, J.R., Masegosa, A.D. & García, I.J. A cooperative strategy for solving dynamic optimization problems. Memetic Comp. 3, 3–14 (2011). https://doi.org/10.1007/s12293-010-0031-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12293-010-0031-x

Keywords

Navigation