Turkish Journal of Electrical Engineering and Computer Sciences




The imperialist competitive algorithm (ICA) is a recent global search strategy developed based on human social evolutionary phenomena in the real world. However, the ICA has the drawback of trapping in local optimum solutions when used for high-dimensional or complex multimodal functions. In order to deal with this situation, in this paper an improved ICA, named GICA, is proposed that can enhance ICA performance by using a new assimilation method and establishing a relationship between countries inspired by the globalization concept in the real world. The proposed algorithm is evaluated using a set of well-known benchmark functions for global optimization. Obtained results show the efficiency and effectiveness of the method and show that this strategy can deal with the local optimum problem.


Imperialist competitive algorithm, optimization, local optimum, globalization, segmented assimilation, crossover

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