Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Nguyen, TT; Jenkinson, I; Yang, Z
Publisher: IEEE
Languages: English
Types: Article
Subjects: QA75
In this paper we propose a novel evolutionary algorithm that is able to adaptively separate the explored and unexplored areas to facilitate detecting changes and tracking the moving optima. The algorithm divides the search space into multiple regions, each covers one basin of attraction in the search space and tracks the corresponding moving optimum. A simple mechanism was used to estimate the basin of attraction for each found optimum, and a special data structure named KD-Tree was used to memorise the searched areas to speed up the search process. Experimental results show that the algorithm is competitive, especially against those that consider change detection an important task in dynamic optimisation. Compared to existing multi-population algorithms, the new algorithm also offers less computational complexity in term of identifying the appropriate sub-population/region for each individual.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] T. T. Nguyen, “Continuous Dynamic Optimisation Using Evolutionary Algorithms,” Ph.D. dissertation, School of Computer Science, University of Birmingham, January 2011, http://etheses.bham.ac.uk/1296.
    • [2] T. T. Nguyen, S. Yang, and J. Branke, “Evolutionary dynamic optimization: A survey of the state of the art,” Swarm and Evolutionary Computation, vol. 6, pp. 1 - 24, 2012.
    • [3] S. Yang and C. Li, “A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments,” IEEE Trans. Evolutionary Computation, vol. 14, no. 6, pp. 959-974, 2010.
    • [4] R. Lung and D. Dumitrescu, “Evolutionary swarm cooperative optimization in dynamic environments,” Natural Computing, vol. 9, no. 1, pp. 83-94, 2010.
    • [5] V. Noroozi, A. Hashemi, and M. Meybodi, “Cellularde: A cellular based differential evolution for dynamic optimization problems,” in Adaptive and Natural Computing Algorithms. Springer, 2011, pp. 340-349.
    • [6] R. Mendes and A. Mohais, “Dynde: a differential evolution for dynamic optimization problems,” in Congress on Evolutionary Computation. IEEE, 2005, pp. 2808-2815.
    • [7] W. Du and B. Li, “Multi-strategy ensemble particle swarm optimization for dynamic optimization,” Information Sciences, vol. 178, no. 15, pp. 3096 - 3109, 2008.
    • [8] T. T. Nguyen and X. Yao, “Continuous dynamic constrained optimisation - the challenges,” IEEE Transactions on Evolutionary Computation, vol. 16, no. 6, pp. 769-786, 2012.
    • [9] H. Richter, “Detecting change in dynamic fitness landscapes,” in Congress on Evolutionary Computation, 2009, pp. 1613-1620.
    • [10] J. J. Grefenstette, “Genetic algorithms for changing environments,” in Parallel Problem Solving from Nature 2, 1992, pp. 137-144.
    • [11] T. Blackwell and J. Branke, “Multiswarms, exclusion, and anticonvergence in dynamic environments.” IEEE Trans. Evolutionary Computation, vol. 10, no. 4, pp. 459-472, 2006.
    • [12] F. Oppacher and M. Wineberg, “The shifting balance genetic algorithm: Improving the ga in a dynamic environment,” in Genetic and Evolutionary Computation Conference, 1999, pp. 504-510.
    • [13] J. Branke, T. Kaußler, C. Schmidt, and H. Schmeck, “A multi-population approach to dynamic optimization problems,” in Adaptive Computing in Design and Manufacturing 2000. Springer, 2000.
    • [14] R. K. Ursem, “Multinational GA optimization techniques in dynamic environments,” in Genetic and Evolutionary Computation Conference, 2000, pp. 19-26.
    • [15] J. L. Bentley and J. H. Friedman, “Data structures for range searching,” ACM Computing Surveys, vol. 11, no. 4, pp. 397-409, 1979.
    • [16] Wikipedia, “KD-tree,” [accessed Apr-07].
    • [17] T. T. Nguyen and X. Yao, “An experimental study of hybridizing cultural algorithms and local search,” International Journal of Neural Systems, vol. 18, no. 1, pp. 1-18, 2008.
    • [18] --, “Hybridizing cultural algorithms and local search,” in Intelligent Data Engineering and Automated Learning, 2006, pp. 586-594.
    • [19] J. Branke, Evolutionary Optimization in Dynamic Environments. Kluwer, 2001.
    • [20] J. Branke and H. Schmeck, “Designing evolutionary algorithms for dynamic optimization problems,” in Theory and Application of Evolutionary Computation: Recent Trends. Springer, 2003, pp. 239-262.
    • [21] I. Moser and T. Hendtlass, “A simple and efficient multi-component algorithm for solving dynamic function optimisation problems,” in Proceedings of the IEEE Congress on Evolutionary Computation CEC'07., 2007, pp. 252-259.
    • [22] C. Li and S. Yang, “A general framework of multipopulation methods with clustering in undetectable dynamic environments,” IEEE Transactions on Evolutionary Computation, vol. 16, no. 4, pp. 556-577, 2012.
    • [23] T. Blackwell, “Particle swarm optimization in dynamic environment,” in Evolutionary Computation in Dynamic and Uncertain Environments. Springer-Verlag, 2007, pp. 28-49.
    • [24] L. Bui, M.-H. Nguyen, J. Branke, and H. Abbass, “Tackling dynamic problems with multiobjective evolutionary algorithms,” in Multiobjective Problem Solving from Nature. Springer, 2008, pp. 77-91.
    • [25] M. C. du Plessis and A. P. Engelbrecht, “Using competitive population evaluation in a differential evolution algorithm for dynamic environments,” European Journal of Operational Research, vol. 218, no. 1, pp. 7 - 20, 2012.
    • [26] M. Kamosi, A. Hashemi, and M. Meybodi, “A new particle swarm optimization algorithm for dynamic environments,” in Swarm, Evolutionary, and Memetic Computing. Springer, 2010, pp. 129-138.
    • [27] J. Brest, A. Zamuda, B. Boskovic, M. Maucec, and V. Zumer, “Dynamic optimization using self-adaptive differential evolution,” in IEEE Congress on Evolutionary Computation, 2009, pp. 415-422.
  • No related research data.
  • No similar publications.

Share - Bookmark

Download from

Cite this article