EVOLUTIONARY ALGORITHMS FOR THE PROBLEM OF OPTIMAL CONTROL
- 作者: Diveev AI1,2, Konstantinov SV2
-
隶属关系:
- Institution of Russian Academy of Sciences Dorodnicyn Computing Centre of RAS
- Peoples’ Friendship University of Russia (RUDN University)
- 期: 卷 18, 编号 2 (2017)
- 页面: 254-265
- 栏目: CYBERNETICS AND MECHATRONICS
- URL: https://journal-vniispk.ru/2312-8143/article/view/335348
- DOI: https://doi.org/10.22363/2312-8143-2017-18-2-254-265
- ID: 335348
如何引用文章
全文:
详细
The paper describes some of the popular evolutionary algorithms: genetic algorithms, differential evolution method, particle swarm optimization and bat-inspired method. With the help of these algorithms the problem of optimal control of a mobile robot is solved. For comparison the same problem is solved with the algorithm of fast gradient descent and random search. The computational experiments showed that evolutionary algorithms provide more accurate results for the optimal control problems than fast gradient descent algorithm.
作者简介
A Diveev
Institution of Russian Academy of Sciences Dorodnicyn Computing Centre of RAS; Peoples’ Friendship University of Russia (RUDN University)
编辑信件的主要联系方式.
Email: aidiveev@mail.ru
Doctor of technical sciences, professor, chief of sector of Cybernetic problems, Federal Research Centre “Computer Science and Control” of Russia Academy of Sciences, professor of department Mechanics and mechatronics, Engineering Academy, Peoples’ Friendship University of Russia (RUDN University)
Vavilova str., 40, Moscow, Russia, 119333; Miklukho-Maklaya str., 6, Moscow, Russia, 117198S Konstantinov
Peoples’ Friendship University of Russia (RUDN University)
Email: konstantinov_sv@rudn.university
senior lecturer of department Mechanics and mechatronics, Engineering Academy, Peoples’ Friendship University of Russia (RUDN University). Research interests: Optimization algorithms, evolutionary algorithms, genetic algorithms, computational methods for problems of optimal control
Miklukho-Maklaya str., 6, Moscow, Russia, 117198参考
- Polak E. Chislennye metody optimizatsii. M.: Mir, 1974. (In Russ).
- Karpenko A.P. Sovremennye algoritmy poiskovoi optimizatsii. Algoritmy, vdokhnovlennye prirodoi. M.: Izdatel’stvo MGTU im. N.E. Baumana, 2014. (In Russ).
- Goldberg D.E. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, 1989.
- Storn R., Price K. Differential Evolution — A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces / Journal of Global Optimization. 1997. No. 11. P. 341—359.
- Kennedy J., Eberhart R. Particle Swarm Optimization / Proceedings of IEEE International Conference on Neural Networks IV. 1995. P. 1942—1948.
- Yang Xin-She. A New Metaheuristic Bat-Inspired Algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010). Studies in Computational Intelligence. Berlin: Springer, 2010. Vol. 284. P. 65—74.
- Karpenko A.P. Populyatsionnye algoritmy global’noi poiskovoi optimizatsii. Obzor novykh i maloizvestnykh algoritmov. Informatsionnye tekhnologii. 2012. No. 7 P. 1—32. (In Russ).
- Panteleev A.V., Letova T.A. Metody optimizatsii v primerakh i zadachakh: ucheb. posobie. M.: Vysshaya shkola, 2005. (In Russ).
补充文件
