Abstract
Despite the success of the imperialist competitive algorithm (ICA) in solving optimization problems, it still suffers from frequently falling into local minima and low convergence speed. In this paper, a fuzzy version of this algorithm is proposed to address these issues. In contrast to the standard version of ICA, in the proposed algorithm, powerful countries are chosen as imperialists in each step; according to a fuzzy membership function, other countries become colonies of all the empires. In absorption policy, based on the fuzzy membership function, colonies move toward the resulting vector of all imperialists. In this algorithm, no empire will be eliminated; instead, during the execution of the algorithm, empires move toward one point. Other steps of the algorithm are similar to the standard ICA. In experiments, the proposed algorithm has been used to solve the real world optimization problems presented for IEEE-CEC 2011 evolutionary algorithm competition. Results of experiments confirm the performance of the algorithm.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Andreani, R., Birgin, E.G., Martinez, J.M., et al., 2009. Augmented Lagrangian methods under the constant positive linear dependence constraint qualification. Math. Progr., 111(1-2):5–32. [doi:10.1007/s10107-006-0077-1]
Bertsekas, D., Gafni, E., 1983. Projected Newton methods and optimization of multicommodity flows. IEEE Trans. Autom. Contr., 28(12):1090–1096. [doi:10.1109/TAC.1983.1103183]
Brownlee, J., 2011. Clever Algorithms: Nature-Inspired Programming Recipes (1st Ed.). LuLu Enterprises.
Das, S., Suganthan, P.N., 2010. Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems. Technical Report, Jadavpur University, India, and Nanyang Technological University, Singapore.
Davis, L., 1987. Genetic Algorithms and Simulated Annealing. Morgan Kaufman Publishers, Los Altos, CA.
Fishman, G., 1996. Monte Carlo Concepts, Algorithms and Applications. Chapel-Hill, Springer-Verlag, New York.
Gargari, A., Lucas, E., 2007. Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. IEEE Congress on Evolutionary Computation, p.4661–4667. [doi:10.1109/CEC.2007.442 5083]
Golban, C., Nedevschi, S., 2011. Linear vs. nonlinear minimization in stereo visual odometry. IEEE Int. Intelligent Vehicles Symp., p.888–894. [doi:10.1109/IVS.2011.5940537]
Guo, P., Wang, X., Han, Y., 2010. The enhanced genetic algorithms for the optimization design. IEEE Conf. on Biomedical Engineering and Informatics, p.2990–2994. [doi:10.1109/BMEI.2010.5639829]
Kaveh, A., Talatahari, S., 2010. Optimum design of skeletal structures using imperialist competitive algorithm. J. Comput. Struct., 88(21):1220–1229. [doi:10.1016/j.compstruc.2010.06.011]
Kennedy, J., Eberhart, R., 1995. A new optimizer using particle swarm theory. Proceedings 6th International Symposium on Micro Machine and Human Science, p.39–43. [doi:10.1109/MHS.1995.494215]
Nelder, J., Mead, R., 1965. A simplex method for function minimization. Comput. J., 7(4):308–313. [doi:10.1093/comjnl/7.4.308]
Rajabioun, R., 2011. Cuckoo optimization algorithm. Appl. Soft Comput., 11(8):5508–5518. [doi:10.1016/j.asoc.2011.05.008]
Talatahari, S., Azar, F., Sheikholeslami, R., et al., 2012. Imperialist competitive algorithm combined with chaos for global optimization. Commun. Nonl. Sci. Numer. Simul., 17(3):1312–1319. [doi:10.1016/j.cnsns.2011.08.021]
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Arish, S., Amiri, A. & Noori, K. FICA: fuzzy imperialist competitive algorithm. J. Zhejiang Univ. - Sci. C 15, 363–371 (2014). https://doi.org/10.1631/jzus.C1300088
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/jzus.C1300088