Покращення ефективності генетичного алгоритму при оптимізації схем

The work is devoted to the problem of optimization of electronic circuits using a modification of the genetic algorithm (GA) based on the previously developed idea of ​​generalized circuit optimization. In this case, it is possible to overcome one of the main disadvantages of GA, namely premature convergence to local minima, and significantly improve the quality of minimization. In this case, the updated GA generates a set of populations determined by different generalized optimization strategies. Fitness functions, as well as chromosome parameters such as length and structure, are determined by a control vector introduced within the framework of the idea of ​​generalized optimization. The control vector determines the number of independent variables of the optimization problem and serves as the main element for selecting the most promising optimization strategies. Complex strategies obtained by combining different basic strategies can improve the accuracy of the optimization problem and reduce the number of required generations. As a result, it is possible to improve the quality of the resulting solution and significantly reduce the computer time for optimizing the electronic circuit.