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This article is devoted to the development of stochastic methods of global extremum search. The modification of the annealing simulation algorithm [Ermakov and Semenchikov, 2019] is combined with the covariance matrix adaptation method [Ermakov, Kulikov and Leora, 2017]. In this case, an effective computational approach [Ermakov and Mitioglova, 1977] is used for modeling the multivariate normal distribution. The special algorithms of covariance matrices adaptation are suggested to avoid the obtaining a local extremum instead of a global one. The methods proposed are successfully applied to the problem of nonlinear regression parameters calculation. This problem often arises in physics and mathematics and may be reduced to global extremum search. In particular case considered the extremum of ravine function of 14 variables was found.
Original languageEnglish
Article number3
Pages (from-to)13-17
Number of pages5
JournalCybernetics and Physics
Volume11
Issue number1
DOIs
StatePublished - 2 Jun 2022

    Research areas

  • Genetic stochastic algorithms; Global extremum; Covariance matrix; Normal distribution; Nonlinear regression, Nonlinear regres-sion, Normal distribution, Global extremum, Co-variance matrix, Genetic stochastic algorithms

    Scopus subject areas

  • Mathematics(all)
  • Control and Optimization
  • Artificial Intelligence
  • Signal Processing
  • Fluid Flow and Transfer Processes
  • Computer Vision and Pattern Recognition
  • Physics and Astronomy (miscellaneous)

ID: 95649980