Abstract
Introduction. The paper considers some features of the parallel implementation of a multipopulation genetic algorithm, as well as approaches to its optimization. The results of experiments with the use of a different number of processors and different methods of generating initial populations are presented in order to optimize the algorithm according to several criteria (assessment of the use of computational and time resources). On the example of a specific test problem, estimates are given for choosing the optimal number of processors to obtain the desired result. The purpose of this work is to conduct experiments with a given test problem with a different number of processors and alternative methods for generating the initial population to evaluate the effectiveness of the algorithm. Results. For the test problem, to obtain a result of 90–94 % of the optimum, the most efficient in terms of computing resources is the use of 4 processors with an algorithm for uniform scanning of the space of factor values. To achieve a result exceeding 94 % and optimize by K1 (computational resources), 8 processors and an algorithm for uniform scanning of the space of factor values showed the best result. If we also take into account the criterion of time resources K2, then to achieve 90–98 % of the optimum, it is necessary to use 8 processors, for 99–100 % 12 or 16 processes, depending on C1 and C2 (cost of computational and time resources respectively). Conclusions. Performed experiments show that the algorithm of uniform scanning of the space of factor values is more efficient than the random method of generating the initial population. Experiments also showed that in order to achieve the maximum efficiency of PMGA, the number of processors must be chosen depending on the desired result precision. Keywords: parallel genetic algorithm, initial population generation, choice of the number of processors (populations), algorithm optimization.
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