A Comparison of Evolutionary Algorithms and Gradient-based Methods for the Optimal Control Problem

An experimental comparison of evolutionary algorithms and gradient-based methods for the optimal control problem is carried out. The problem is solved separately by Particle swarm optimization, Grey wolf optimizer, Fast gradient descent method, Marquardt method and Adam method. The simulation is performed on a jet aircraft model. The results of each algorithm performance are compared according to the best found value of the fitness function, the mean value and the standard deviation. © 2018 IEEE.

Редакторы
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Издательство
Institute of Electrical and Electronics Engineers Inc.
Номер выпуска
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Язык
Английский
Страницы
259-264
Статус
Опубликовано
Подразделение
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Ссылка
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Номер
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Том
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Год
2018
Организации
  • 1 RUDN University, Federal Research Centre 'Computer Science and Control' of Russian Academy of Sciences, Moscow, Russian Federation
  • 2 RUDN University, Moscow, Russian Federation
  • 3 Federal Research Centre 'Computer Science and Control' of Russian Academy of Sciences, Russian Federation
Ключевые слова
Particle swarm optimization (PSO); Algorithm performance; Experimental comparison; Fitness functions; Gradient Descent method; Gradient-based method; Grey Wolf Optimizer; Optimal control problem; Standard deviation; Optimal control systems
Дата создания
19.10.2018
Дата изменения
19.10.2018
Постоянная ссылка
https://repository.rudn.ru/ru/records/article/record/6575/