SEMCCO 2012: Swarm, Evolutionary, and Memetic Computing pp 611-619 | Cite as
A Strategy Pool Adaptive Artificial Bee Colony Algorithm for Dynamic Environment through Multi-population Approach
Abstract
Swarm Intelligence is based on developing metaheuristics that are modeled on certain life-sustaining principles exhibited by the biotic components of the ecosystem. There has been a surge in interest for nature inspired computing for devising more efficient models that can find solution to real-world problems using minimal resources at disposal. In this paper, an enhanced version of Artificial Bee Colony algorithm have been proposed that takes on the task of finding the optimal solution in a continuously changing (dynamic) solution space by incorporating a pool of varied perturbation strategies that operate on a multi-population group and synergizing the strategy pool with a set of diversity-inclusion techniques that help to maintain population diversity.
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References
- 1.Talbi, E.G.: Metaheuristics-From Design to implementation. John Wiley and Sons (2009)Google Scholar
- 2.Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)Google Scholar
- 3.Engelbrecht, A.: Fundamentals of Computational Swarm Intelligence. John Wiley and Sons, UK (2005)Google Scholar
- 4.Clerc, M., Kennedy, J.: The particle swarm-explosion, stability and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 58–73 (2002)Google Scholar
- 5.Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kauffman, San Francisco (2001)Google Scholar
- 6.Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaldi, M.: The Bees Algortihm – A Novel Tool for Complex Optimization Problems. In: Proceedings of IPROMS 2006 Conference, pp. 454–461 (2006)Google Scholar
- 7.Karaboga, D.: An idea based on honey bee swarm for numerical optimization, Technical Report TR 06, Erciyes University, Engg. Faculty, Computer Engineering Department (2005)Google Scholar
- 8.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)Google Scholar
- 9.Blackwell, T., Branke, J.: Multi swarms, Exclusion and Anti convergence in dynamic environments. IEEE Transactions on Evolutionary Computation, 459–472 (2004)Google Scholar
- 10.Lee, C.Y., Yao, X.: Evolutionary Programming using Mutation based on the Levy probability distribution. IEEE Transactions on Evolutionary Computation, 1–13 (2004)Google Scholar
- 11.Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: IEEE Congress on Evolutionary Computation (1999)Google Scholar
- 12.Yang, S., Ong, Y.S., Jin, Y.: Evolutionary Computation in Dynamic and Uncertain Environment. Springer, Berlin (2007)CrossRefGoogle Scholar
- 13.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, Univ. of Leicester, Univ. of Birmingham, Nanyang Technological University, Tech. Rep. (2008)Google Scholar
- 14.Korosec, P., Silc, J.: The differential ant-stigmergy algorithm applied to dynamic optimization problems. In: IEEE Congress on Evolutionary Computation, pp. 407–410 (2009)Google Scholar
- 15.de Franca, F.O., Von Zuben, F.J.: A dynamic artificial immune algorithm applied to challenging benchmarking problems. In: IEEE Cong. on Evo. Computation, pp. 423–430 (2009)Google Scholar
- 16.Yang, S., Li, C.: A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments. IEEE Trans. on Evo. Comp. 14(6) (2010)Google Scholar
- 17.Mendes, R., Mohais, A.S.: DynDE: a differential evolution for dynamic optimization problems. In: IEEE Congress on Evolutionary Computation, pp. 2808–2815 (2005)Google Scholar