SOLUTION OF THE ELEMENTS LOCATION PROBLEM IN DIGITAL COMPUTER EQUIPMENT ON THE BASIS OF INTEGRATION OF EVOLUTIONARY SEARCH AND FUZZY CONTROL METHODS
The problem of elements placement in digital computing equipment are consider in the article. The analysis of the current state of research on this topic is carried out, the relevance of the problem under consideration is noted. The importance of developing new effective methods for solving engineering design problems is emphasized. The prospects of developing and using hybrid approaches and models for solving complex semi-formalized design and optimization problems are noted. The statement of the problem of placement of circuit elements of digital computing equipment is given. The importance of a qualitative solution of the placement problem from the point of view of the successful implementation of the subsequent stages of design is noted. The analysis of various approaches and algorithms for solving the placement problem is carried out. Options for choosing various criteria for assessing the quality of placement are given. A complex additive criterion for assessing the quality of placement is proposed. The objective function and limitations of the considered placement problem as an optimization problem are given. A hybrid approach to solving the placement problem is proposed. To increase the efficiency and reduce the running time of the algorithm, a model of a parallel multipopulation genetic algorithm is proposed. To synchronize evolutionary processes in a multipopulation genetic algorithm, a modified migration operator has been developed. An analysis of the efficiency of the proposed migration operator has been carried out and recommendations for its use have been formulated. In order to increase the speed of the algorithm for solving the placement problem, a model for organizing parallel evolutionary computations through the use of multithreading at the local level is proposed. The principles of operation of the fuzzy control module are described. The procedure of logical inference using the rule base is described. The structure of a multilayer neural network that implements the Gaussian function is proposed. A model of a fuzzy logic controller for dynamically changing the values of control parameters of a genetic algorithm is proposed. The control parameters of the fuzzy logic controller are determined. The proposed hybrid algorithm is implemented as an application program. A series of computational experiments were carried out to determine the efficiency of the developed algorithm and to select the optimal values of the control parameters.