Abstract
This paper introduces an improved evolutionary algorithm based ontheImperialist Competitive Algorithm (ICA), called Quad Countries Algorithm (QCA). The Imperialist Competitive Algorithm is inspired by socio-political process of imperialistic competition in the real world and has shown its reliable performance in optimization problems. In the ICA, the countries are classified into two groups: Imperialists and Colonies. However, in the QCA, two other kinds of countries including Independent and Seeking Independence are added to the countries collection. In the ICA also the Imperialists’ positions are fixed, while in the QCA Imperialists may move. The proposed algorithm was tested by well-known benchmarks, and the compared results of the QCA with results of ICA, GA [12], PSO [12], PS-EA [12] and ABC [11] show that the QCA has better performance than all mentioned algorithms. Among them, the QCA, ABC and PSO have better performance respectively in 50%, 41.66% and 8.33% of all cases.
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References
Sarimveis, H., Nikolakopoulos, A.: A Life Up Evolutionary Algorithm for Solving Nonlinear Constrained Optimization Problems. Computer & Operation Research 32(6), 1499–1514 (2005)
Mühlenbein, H., Schomisch, M., Born, J.: The Parallel Genetic Algorithm as Function Optimizer. In: Proceedings of The Forth International Conference on Genetic Algorithms, pp. 270–278. University of California, San diego (1991)
Holland, J.H.: ECHO: Explorations of Evolution in a Miniature World. In: Farmer, J.D., Doyne, J. (eds.) Proceedings of the Second Conference on Artificial Life (1990)
Melanie, M.: An Introduction to Genetic Algorithms. MIT Press, Massachusett’s (1999)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. Proceedings of IEEE, 1942–1948 (1995)
Atashpaz-Gargari, E., Lucas, C.: Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition. In: IEEE Congress on Evolutionary Computation (CEC 2007), pp. 4661–4667 (2007)
Atashpaz-Gargari, E., Hashemzadeh, F., Rajabioun, R., Lucas, C.: Colonial Competitive Algorithm: A novel approach for PID controller design in MIMO distillation column process. International Journal of Intelligent Computing and Cybernetics (IJICC) 1(3), 337–355 (2008)
Zhang, Y., Wang, Y., Peng, C.: Improved Imperialist Competitive Algorithm for Constrained Optimization. International Forum on Computer Science-Technology and Applications (2009)
Bahrami, H., Feaz, K., Abdechiri, M.: Imperialist Competitive Algorithm using Chaos Theory for Optimization (CICA). In: Proceedings of the 12th International Conference on Computer Modelling and Simulation (2010)
Bahrami, H., Feaz, K., Abdechiri, M.: Adaptive Imperialist Competitive Algorithm (AICA). In: Proceedings of The 9th IEEE International Conference on Cognitive Informatics, ICCI 2010 (2010)
Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization 39(3), 459–471 (2007)
Srinivasan, D., Seow, T.H.: Evolutionary Computation. In: CEC 2003, Canberra, Australia, December 8-12, vol. 4, pp. 2292–2297 (2003)
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Soltani-Sarvestani, M.A., Lotfi, S., Ramezani, F. (2012). Quad Countries Algorithm (QCA). In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28493-9_14
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DOI: https://doi.org/10.1007/978-3-642-28493-9_14
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