Sistema Inmune Artificial con Población Reducida para Optimización Numérica

Abstract

In this thesis, we present a new algorithm, this is a micro-artificial immune system (micro-AIS) based on the Clonal Selection Theory for solving numerical optimization problems. For our study we consider the algorithm named CLONALG, it is a widely used artificial immune system. During the process of cloning, CLONALG greatly increases the size of its population, so this feature is attractive to propose a version with a reduced population. Our hypothesis is that by reducing the number of individuals in a population will decrease the number of evaluations to the objective function, increasing the speed of convergence and reducing the use of data memory. Our proposal uses a population of 5 individuals (antibodies) which are obtained only 15 clones. In the maturation stage of the clones, two simple and fast mutation operators are used in a nominal convergence that works together with a reinitialization process to preserve the diversity. The SIA does not use a cross operator. To validate our algorithm, we use a set of test functions taken from the specialized literature to compare our approach with the standard version of CLONALG. We made two versions of the micro-AIS, one that does not handle constraints and one that will allow them to deal with, whereas in the real world most optimization problems have constraints. It also presents two approaches of micro-AIS embedded hardware running intrinsically to solve the maxone problem, first implemented on a commercial microcontroller and the second in a reconfigurable logic device (FPGA).