Abstract:A method for the automatic determination of crystal structures using powder diffraction data by the multipopulation genetic algorithm is proposed. The advantage of using coevolution with exchange by better individuals over using evolution within a single genetic algorithm without interpopulation exchange is demonstrated. The process of searching for a structural solution using the multipopulation genetic algorithm is illustrated and analyzed by the example of the known Ca 2 Al 3 O 6 F crystal structure (sp. gr… Show more
The problem of global optimization arises in various fields of science and technology, and several different ways of solving it have been proposed. The results of the study of the effectiveness of the non-parametric global optimization algorithm are presented. A comparative analysis of this algorithm is presented. performance analysis of the algorithm based on the Ackley, Rastrigin, Shekel, Griewank and Rosenbrock function. In addition, studies were carried out for the three initial points of the distribution algorithms: the sequence LPτ, the sequence UDC, the uniform random distribution. thus, the best way to initialize the initial points of the non-parametric optimization algorithm on these test functions was identified. According to the research results, the effective parameters of the genetic algorithm were established.
The problem of global optimization arises in various fields of science and technology, and several different ways of solving it have been proposed. The results of the study of the effectiveness of the non-parametric global optimization algorithm are presented. A comparative analysis of this algorithm is presented. performance analysis of the algorithm based on the Ackley, Rastrigin, Shekel, Griewank and Rosenbrock function. In addition, studies were carried out for the three initial points of the distribution algorithms: the sequence LPτ, the sequence UDC, the uniform random distribution. thus, the best way to initialize the initial points of the non-parametric optimization algorithm on these test functions was identified. According to the research results, the effective parameters of the genetic algorithm were established.
The purpose of this work is to summarize the results of research concerning the application of genetic algorithms, since in solving problems of complex systems optimization situations often make it difficult or impossible to use classical methods. To solve this problem, research is carried out on the functions of Akli, Rastrigin, Shekel, complaints handling functions and Rosenbrock functions. The studies are conducted on three starting point scattering algorithms: LPτ sequence, UDC sequences and universal random variation. As a result of the analysis, the option of initialization, selection, recombination, mutation and coding of this algorithm according to given test functions for the data of the scatter of initial points is chosen. The effective parameters of the genetic algorithm according to the results of research are established.
The use of randomness in the spread of points in the R40 space gives doubts about the stability of using these spreads and the stability of global optimization algorithms predictions that are based on these spreads. The uniformity of the following initial points scatter algorithms is analyzed: LPτ sequence, UDC sequence, uniform random spread in R40. The uniformity of the spread was determined by the distance of the points from the centers of the grid cells in two-dimensional coordinate planes of the R40 cube space and by the uniformity of the projections of the points on the coordinate axes in these planes. The authors identified the features of using the points spread algorithms when the number of points is multiple to two and not multiple to two. The UDC sequence is the best initial point spread algorithm in the R40 space by two uniformity factors. LPτ sequence is at the second place and recently used uniform random scatter is at the third place.
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