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.

Authors
Diveev A.I. 1 , Konstantinov S.V. 2 , Sofronova E.A. 3
Conference proceedings
2018 5th International Conference on Control, Decision and Information Technologies, CoDIT 2018
Publisher
Institute of Electrical and Electronics Engineers Inc.
Language
English
Pages
259-264
Status
Published
DOI
10.1109/CoDIT.2018.8394805
Year
2018
Organizations
  • 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
Keywords
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
Date of creation
19.10.2018
Date of change
19.10.2018
Short link
https://repository.rudn.ru/en/records/article/record/6575/
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