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Imperialist Competitive Algorithm (ICA)

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The code of recently introduced optimization strategy, Imperialist Competitive Algorithm (ICA).

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Evolutionary optimization methods, inspired from natural processes, have shown good performance in solving complex optimization problems. For example, genetic algorithms (inspired from biological evolution of human and other species), ant colony optimization (based on ants effort to find optimal path to the food source) and simulated annealing (based on real annealing process in which a substance is heated over its melting point and then cooled to reach to a crystalline lattice) are widely used to solve engineering optimization problems.
The proposed evolutionary optimization algorithms are generally inspired by modeling the natural processes and other aspects of species evolution, especially human evolution, are not considered. The method, proposed in this work, uses socio-political evolution of human as a source of inspiration for developing a powerful optimization strategy. Especially, this algorithm considers the imperialism as a level of human’s social evolution and by mathematically modeling this complicated political and historical process, harnesses it as tool for evolutionary optimization. Since its recent inception this novel method has been widely adopted by researchers to solve different optimization tasks. This method is used to design optimal layout for factories, adaptive antenna arrays, intelligent recommender systems, optimal controller for industrial and chemical possesses.

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MATLAB release MATLAB 6.1 (R12.1)
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03 Jun 2014 a

good

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