Email this article Study of the Practical Convergence of Evolutionary Algorithms for the Optimal Program Control of a Wheeled Robot
- Authors: Diveev A.I.1,2, Konstantinov S.V.2
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Affiliations:
- Federal Research Center Computer Science and Control
- Peoples’ Friendship University of Russia (RUDN University)
- Issue: Vol 57, No 4 (2018)
- Pages: 561-580
- Section: Optimal Control
- URL: https://journal-vniispk.ru/1064-2307/article/view/220161
- DOI: https://doi.org/10.1134/S106423071804007X
- ID: 220161
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Abstract
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.
About the authors
A. I. Diveev
Federal Research Center Computer Science and Control; Peoples’ Friendship University of Russia (RUDN University)
Author for correspondence.
Email: aidiveev@mail.ru
Russian Federation, Moscow; Moscow
S. V. Konstantinov
Peoples’ Friendship University of Russia (RUDN University)
Email: aidiveev@mail.ru
Russian Federation, Moscow
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