Optimization Process by Generalized Genetic Algorithm

Abstract

The approach developed earlier, based on generalized optimization, was successfully applied to the problem of designing electronic circuits using deterministic optimization methods. In this paper, a similar approach is extended to the problem of optimizing electronic circuits using a genetic algorithm (GA) as the main optimization method. The fundamental element of generalized optimization is an artificially introduced control vector that generates many different strategies within the optimization process and determines the number of independent variables of the optimization problem, as well as the length and structure of chromosomes in the GA. In this case, the GA forms a set of populations defined by a fitness function specified in different ways depending on the strategy chosen within the framework of the idea of generalized optimization. The control vector allows you to generate different strategies, as well as build composite strategies that significantly increase the accuracy of the resulting solution. This, in turn, makes it possible to reduce the number of generations required during the operation of the GA and reduce the processor time by 3–5 orders of magnitude when solving the circuit optimization problem compared to the traditional GA. An analysis of the optimization procedure for some electronic circuits showed the effectiveness of this approach. The obtained results prove that the applied modification of the GA makes it possible to overcome premature convergence and increase the minimization accuracy by 3-4 orders of magnitude.