Abstract
In real life we are often confronted with dynamic optimization problems whose optima change over time. These problems challenge traditional optimization methods as well as conventional evolutionary optimization algorithms. In this paper, we propose an evolutionary model that combines the differential evolution algorithm with cellular automata to address dynamic optimization problems. In the proposed model, called CellularDE, a cellular automaton partitions the search space into cells. Individuals in each cell, which implicitly create a subpopulation, are evolved by the differential evolution algorithm to find the local optimum in the cell neighborhood. Experimental results on the moving peaks benchmark show that CellularDE outperforms DynDE, cellular PSO, FMSO, and mQSO in most tested dynamic environments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yang, S.: Genetic algorithms with memory-and elitism-based immigrants in dynamic environments. Evolutionary Computation 16, 385–416 (2008)
Grefenstette, J.J.: Genetic algorithms for changing environments. In: Männer, R., Manderick, B. (eds.) Proc. of the 2nd Int. Conf. on Parallel Problem Solving from Nature, pp. 137–144 (1992)
Goldberg, D., Smith, R.: Nonstationary function optimization using genetic algorithms with dominance and diploidy, pp. 59–68. L. Erlbaum Associates Inc., Mahwah (1987)
Yang, S.: Non-stationary problem optimization using the primal-dual genetic algorithm, vol. 3, pp. 2246–2253. IEEE, Los Alamitos (2004)
Cobb, H.G., Grefenstette, J.J.: Genetic Algorithms for Tracking Changing Environments. In: Proceedings of the 5th International Conference on Genetic Algorithms, June 01, pp. 523–530 (1993)
Morrison, R., De Jong, K.: Triggered hypermutation revisited, vol. 2, pp. 1025–1032. IEEE, Los Alamitos (2002)
Mendes, R., Mohais, A.: DynDE: a differential evolution for dynamic optimization problems, vol. 3, pp. 2808–2815. IEEE, Los Alamitos (2005)
Parrott, D., Li, X.: Locating and tracking multiple dynamic optima by a particle swarm model using speciation. IEEE Transactions on Evolutionary Computation 10, 440–458 (2006)
Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments-a survey. IEEE Transactions on Evolutionary Computation 9, 303–317 (2005)
Moser, I., Chiong, R.: Dynamic function optimisation with hybridised extremal dynamics. Memetic Computing 2, 137–148 (2010)
Storn, R., Price, K.: Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization 11, 341–359 (1997)
Price, K., Storn, R., Lampinen, J.: Differential evolution: a practical approach to global optimization. Springer, Heidelberg (2005)
du Plessis, M., Engelbrecht, A.: Improved differential evolution for dynamic optimization problems, pp. 229–234. IEEE, Los Alamitos (2008)
Brest, J., Zamuda, A., Boskovic, B., Maucec, M., Zumer, V.: Dynamic optimization using self-adaptive differential evolution, pp. 415–422. IEEE, Los Alamitos (2009)
Kanlikilicer, A., Keles, A., Uyar, A.: Experimental analysis of binary differential evolution in dynamic environments, pp. 2509–2514. ACM, New York (2007)
Hashemi, A.B., Meybodi, M.: A multi-role cellular PSO for dynamic environments, pp. 412–417. IEEE, Los Alamitos (2009), http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5349615
Hashemi, A.B., Meybodi, M.: 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)
Li, C., Yang, S.: Fast multi-swarm optimization for dynamic optimization problems, vol. 7, pp. 624–628. IEEE, Los Alamitos (2008)
Blackwell, T., Branke, J.: Multiswarms, exclusion, and anti-convergence in dynamic environments. IEEE Transactions on Evolutionary Computation 10, 459–472 (2006)
Blackwell, T.: Particle swarm optimization in dynamic environments. In: Evolutionary Computation in Dynamic and Uncertain Environments, pp. 29–49 (2007)
Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems, vol. 3. IEEE, Los Alamitos (2002)
Branke, J.: Evolutionary optimization in dynamic environments. Kluwer Academic Publishers, Norwell (2001)
Fredkin, E.: An informational process based on reversible universal cellular automata. Physica D: Nonlinear Phenomena 45, 254–270 (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Noroozi, V., Hashemi, A.B., Meybodi, M.R. (2011). CellularDE: A Cellular Based Differential Evolution for Dynamic Optimization Problems. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20282-7_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-20282-7_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20281-0
Online ISBN: 978-3-642-20282-7
eBook Packages: Computer ScienceComputer Science (R0)