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Human immunodeficiency virus (HIV) infection may cause death by damaging some of the patient's vital organs through weakening the body's immune system, including CD 4 + T cells. In the meantime, mathematical models can be useful in dealing with this deadly virus by providing effective strategies based on the examination of different infection states. The major aim pursued in this paper is to present a computational algorithm for solving nonlinear systems of ordinary and partial differential equations resulting from the HIV infection models of CD 4 + T cells. The offered method is developed according to the use of local radial basis functions (LRBFs) as shape functions in the discrete collocation scheme. The new technique approximates the solution by a small set of nodes instead of all points located in the domain where the HIV mathematical model is given. Thus the presented method uses less computing volume compared to the globally supported radial basis functions, and as a result, its algorithm can be quickly run on a computer with relatively low memory. The computational efficiency of the scheme is studied by several test examples. [ABSTRACT FROM AUTHOR]

In this paper, we present an algorithm inspired on the T-Cell model of the immune system (i.e., an artificial immune system), which is used to solve economic dispatch problems. The proposed approach is called IA_EDP, which stands for Immune Algorithm for Economic Dispatch Problem , and it uses two versions of a redistribution power operator which tries to keep feasible the solutions that it finds. The proposed approach is validated using eight problems taken from the specialized literature. Our results are compared with respect to those obtained by several other approaches. We also perform some statistical analysis in order to determine the sensitivity of our proposed approach to its parameters. [ABSTRACT FROM AUTHOR]