Abstract
Modern day real world applications present us challenging instances where the system needs to adapt to a changing environment without any sacrifice in its optimality. This led researchers to lay the foundations of dynamic problems in the field of optimization. Literature shows different approaches undertaken to tackle the problem of dynamic environment including techniques like diversity scheme, memory, multi-population scheme etc. In this paper we have proposed a hybrid scheme by combining k-means clustering technique with modified Artificial Bee Colony (ABC) algorithm as the base optimizer and it is expected that the clusters locate the optima in the problem. Experimental benchmark set that appeared in IEEE CEC 2009 has been used as test-bed and our ClPABC (Clustering Particle ABC) algorithm is compared against 4 state-of-the-art algorithms. The results show the superiority of our ClPABC approach on dynamic environment.
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References
Ghazali Talbi, E.: Metaheuristics-From Design to implementation. John Wiley and Sons (2009)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)
Engelbrecht, A.: Fundamentals of Computational Swarm intelligence. John Wiley and Sons, UK (2005)
Clerc, M., Kennedy, J.: The particle swarm-explosion, stability and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 58–73 (2002)
Kennedy, J., Eberhart, R.: Swarm Intelligence. Morgan Kauffman, San Francisco (2001)
Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing 8, 687–697 (2008)
Karaboga, D., Basturk, B.: A powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm. Journal of Global Optimization 39(3) (2007)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: A review. ACM Computing Survey, 264–333 (1999)
Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: IEEE Congress on Evolutionary Computation (1999)
Yang, S., Ong, Y.S., Jin, Y.: Evolutionary Computation in Dynamic and Uncertain Environment. Springer, Berlin (2007)
Li, C., Yang, S., Nguyen, T.T., Yu, E.L., Yao, X., Jin, Y., Beyer, H.G., Suganthan, P.N.: Benchmark Generator for CEC 2009 Competition on Dynamic Optimization, University of Leicester, University of Birmingham, Nanyang Technological University, Technical Report (2008)
Korosec, P., Silc, J.: The differential ant-stigmergy algorithm applied to dynamic optimization problems. In: IEEE Congress on Evolutionary Computation, pp. 407–410 (2009)
de Franca, F.O., Von Zuben, F.J.: A dynamic artificial immune algorithm applied to challenging benchmarking problems. In: IEEE Congress on Evolutionary Computation, pp. 423–430 (2009)
Yang, S., Li, C.: A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments. IEEE Transactions on Evolutionary Computation 14(6) (2010)
Mendes, R., Mohais, A.S.: DynDE: a differential evolution for dynamic optimization problems. In: IEEE Congress on Evolutionary Computation, pp. 2808–2815 (2005)
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Biswas, S., Bose, D., Kundu, S. (2012). A Clustering Particle Based Artificial Bee Colony Algorithm for Dynamic Environment. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_19
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DOI: https://doi.org/10.1007/978-3-642-35380-2_19
Publisher Name: Springer, Berlin, Heidelberg
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