Study of the Practical Convergence of Evolutionary Algorithms for the Optimal Program Control of a Wheeled Robot
- 作者: Diveev A.1,2, Konstantinov S.2
-
隶属关系:
- Federal Research Center Computer Science and Control
- Peoples’ Friendship University of Russia (RUDN University)
- 期: 卷 57, 编号 4 (2018)
- 页面: 561-580
- 栏目: Optimal Control
- URL: https://journals.rcsi.science/1064-2307/article/view/220161
- DOI: https://doi.org/10.1134/S106423071804007X
- ID: 220161
如何引用文章
详细
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.
作者简介
A. Diveev
Federal Research Center Computer Science and Control; Peoples’ Friendship University of Russia (RUDN University)
编辑信件的主要联系方式.
Email: aidiveev@mail.ru
俄罗斯联邦, Moscow; Moscow
S. Konstantinov
Peoples’ Friendship University of Russia (RUDN University)
Email: aidiveev@mail.ru
俄罗斯联邦, Moscow
![](/img/style/loading.gif)