Study of the Practical Convergence of Evolutionary Algorithms for the Optimal Program Control of a Wheeled Robot

Evolutionary algorithms for solving the problem of the optimal program control are considered. The most popular evolutionary algorithms, the genetic algorithm (GA), the differential evolution (DE) algorithm, the particle swarm optimization (PSO), the bat-inspired algorithm (BIA), the bees algorithm (BA), and the grey wolf optimizer (GWO) algorithm are described. An experimental analysis of these algorithms and their comparison with gradient methods are given. An experiment was carried out to solve the problem of the optimal control of a mobile robot with phase constraints. Indicators of the best objective functional value, the average value for several startups, and the standard deviation were used to compare the algorithms. © 2018, Pleiades Publishing, Ltd.

Authors
Diveev A.I. 1, 2 , Konstantinov S.V. 2
Journal
Journal of Computer and Systems Sciences International
Number of issue
4
Language
English
Pages
561-580
Status
Published
Link
External link
DOI
10.1134/S106423071804007X
Volume
57
Year
2018
Organizations
  • 1 Federal Research Center Computer Science and Control, Russian Academy of Sciences, Moscow, Russian Federation
  • 2 Peoples’ Friendship University of Russia (RUDN University), Moscow, Russian Federation
Keywords
Genetic algorithms; Gradient methods; Bees algorithms; Differential evolution algorithms; Experimental analysis; Grey Wolf Optimizer; Optimal controls; Optimal program controls; Phase constraints; Standard deviation; Particle swarm optimization (PSO)
Date of creation
19.10.2018
Date of change
19.10.2018
Short link
https://repository.rudn.ru/en/records/article/record/6556/
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