Abstract
A new hybrid approach to optimization in dynamical environments called Collaborative Evolutionary-Swarm Optimization (CESO) is presented. CESO tracks moving optima in a dynamical environment by combining the search abilities of an evolutionary algorithm for multimodal optimization and a particle swarm optimization algorithm. A collaborative mechanism between the two methods is proposed by which the diversity provided by the multimodal technique is transmitted to the particle swarm in order to prevent its premature convergence. The effect of changing the random number generator used for selection and for variation operators within CESO with a chaotic sequence generator is tested.
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References
T. Blackwell and J. Branke, “Multiswarms, exclusion, and anticonvergence in dynamic environments,” Evolutionary Computation, IEEE Transactions on, vol. 10, issue: 4, 2006.
J. Branke, Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, 2001.
Caponetto, R.; Fortuna, L.; Fazzino, S.; Xibilia, M.G. Chaotic sequences to improve the performance of evolutionary algorithms, Evolutionary Computation, IEEE Transactions on, Vol.7, Iss.3, June 2003 Pages: 289–304
Y. Jin and J. Branke, “Evolutionary optimization in uncertain environments-a survey.” IEEE Trans. Evolutionary Computation, vol. 9, no. 3, pp. 303–317, 2005.
J. Kennedy and R. C. Eberhart, Swarm intelligence. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2001.
X. Li, J. Branke, and T. Blackwell, “Particle swarm with speciation and adaptation in a dynamic environment,” in GECCO’ 06: Proceedings of the 8th annual conference on Genetic and evolutionary computation. New York, NY, USA: ACM Press, 2006, pp. 51–58.
R. I. Lung, D. Dumitrescu. A collaborative model for tracking optima in dynamic environments. In IEEE Congress on Evolutionary Computation, (accepted paper), 2007.
R. Storn and K. Price, “Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces,” Berkeley, CA, Tech. Rep. TR-95-012, 1995.
R. Storn and K. Price, “Differential evolution a simple evolution strategy for fast optimization.” Dr. Dobb’s Journal of Software Tools, vol. 22, no. 4, pp. 18–24, 1997.
R. Thomsen, “Multimodal optimization using crowding-based differential evolution,” in Proceedings of the 2004 IEEE Congress on Evolutionary Computation. Portland, Oregon: IEEE Press, 20–23 June 2004, pp. 1382–1389.
S. Tsutsui, Y. Fujimoto, and A. Gosh, “Forking genetic algorithms: GAs with search space division,” Evolutionary computation, vol. 5, pp. 61–80, 1997.
R. K. Ursem, “Multinational GAs: Multimodal optimization techniques in dynamic environments,” in Proceedings of the Second Genetic and Evolutionary Computation Conference (GECCO-2000), vol. 1. Riviera Hotel, Las Vegas, USA: Morgan Kauffmann Publishers, 2000, pp. 19–26.
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Lung, R.I., Dumitrescu, D. (2007). Collaborative Evolutionary Swarm Optimization with a Gauss Chaotic Sequence Generator. In: Corchado, E., Corchado, J.M., Abraham, A. (eds) Innovations in Hybrid Intelligent Systems. Advances in Soft Computing, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74972-1_28
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DOI: https://doi.org/10.1007/978-3-540-74972-1_28
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