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Abstract
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.
Highlights
The nonparametric global optimization algorithm [1] differs from the others in that it is the most universal global optimization algorithm
LP, the sequence UDC, the uniform random distribution. the best way to initialize the initial points of the non-parametric optimization algorithm on these test functions was identified
The research was conducted on Ackley, Rastrigin, Shekel, Griewank and Rosenbrock function [2]
Summary
The nonparametric global optimization algorithm [1] differs from the others in that it is the most universal global optimization algorithm It doesn't matter for the algorithm what the object of optimization is and how complex the function is, which describes the object. LP sequence [3], UDC sequence, uniform random scatter are very interesting and effective scatter algorithms of initial points. Recent research in this area has been carried out in works [4]. The goal was not to average these parameters, but to test a complex type of a test function on a large number of practical tasks [5].
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