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

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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.

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Authors and Affiliations

  1. Federal Research Center Computer Science and Control, Russian Academy of Sciences, Moscow, Russia

    A. I. Diveev

  2. Peoples’ Friendship University of Russia (RUDN University), Moscow, Russia

    A. I. Diveev & S. V. Konstantinov

Authors
  1. A. I. Diveev
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  2. S. V. Konstantinov
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Correspondence to A. I. Diveev.

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Original Russian Text © A.I. Diveev, S.V. Konstantinov, 2018, published in Izvestiya Akademii Nauk, Teoriya i Sistemy Upravleniya, 2018, No. 4.

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Diveev, A.I., Konstantinov, S.V. Study of the Practical Convergence of Evolutionary Algorithms for the Optimal Program Control of a Wheeled Robot. J. Comput. Syst. Sci. Int. 57, 561–580 (2018). https://doi.org/10.1134/S106423071804007X

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  • DOI: https://doi.org/10.1134/S106423071804007X

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