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FICA: fuzzy imperialist competitive algorithm


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.

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  • 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]

    Article  MathSciNet  Google Scholar 

  • 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]

    Article  MATH  MathSciNet  Google Scholar 

  • Brownlee, J., 2011. Clever Algorithms: Nature-Inspired Programming Recipes (1st Ed.). LuLu Enterprises.

    Google Scholar 

  • 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.

    Google Scholar 

  • Davis, L., 1987. Genetic Algorithms and Simulated Annealing. Morgan Kaufman Publishers, Los Altos, CA.

    MATH  Google Scholar 

  • Fishman, G., 1996. Monte Carlo Concepts, Algorithms and Applications. Chapel-Hill, Springer-Verlag, New York.

    MATH  Google Scholar 

  • 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]

    Google Scholar 

  • 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]

    Google Scholar 

  • 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]

    Google Scholar 

  • 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]

    Article  Google Scholar 

  • 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]

    Google Scholar 

  • Nelder, J., Mead, R., 1965. A simplex method for function minimization. Comput. J., 7(4):308–313. [doi:10.1093/comjnl/7.4.308]

    Article  MATH  Google Scholar 

  • Rajabioun, R., 2011. Cuckoo optimization algorithm. Appl. Soft Comput., 11(8):5508–5518. [doi:10.1016/j.asoc.2011.05.008]

    Article  Google Scholar 

  • 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]

    Article  MATH  Google Scholar 

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Correspondence to Ali Amiri.

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Arish, S., Amiri, A. & Noori, K. FICA: fuzzy imperialist competitive algorithm. J. Zhejiang Univ. - Sci. C 15, 363–371 (2014).

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Key words

  • Optimization problem
  • Imperialist competitive algorithm (ICA)
  • Fuzzy ICA.

CLC number

  • TP301.6