Optimum fuzzy control of human immunodeficiency virus type1 using an imperialist competitive algorithm

Abstract Nowadays the human immunodeficiency virus (HIV), as one of the most dangerous viruses having destructive influences on the human body, attracts a lot of scientists and experts’ attention. By entrance and proliferation of HIV in the T cells, the CD4+ T cells in the blood circulation will be reduced. Recently, many researchers have tried to find a proper mathematical model for the treatment of this deadly virus. As the model becomes more accurate, the treatment protocol is more reliable. In addition, earlier initiation of therapy has been of important clinical benefit to HIV-infected patients. This paper proposes a novel control strategy in order to decrease the level of infected CD4+ T cells and viral load, and increase the level of CD4+ T cells for the treatment of HIV-1-infected patients through a fuzzy-logic-based controller. Furthermore, to optimize the designed control coefficients, the imperialist competitive algorithm has been utilized. Unlike many other optimization algorithms that are inspired by the nature, this algorithm considers the human intelligence and cultural evolutions which are faster than the genetic and somatic evolutions. The simulation results have been shown and compared with those of other works to verify the effectiveness of the proposed strategy.

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