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MATOUŠEK, R. LOZI, R. HŮLKA, T.
Original Title
Stabilization of Higher Periodic Orbits of the Lozi and Hénon Maps using Meta-evolutionary Approaches
Type
conference paper
Language
English
Original Abstract
This paper deals with an advanced adjustment of stabilization sequences for selected discrete chaotic systems by means of meta-evolutionary approaches. As the representative models of deterministic chaotic systems, a two dimensional Lozi map and two dimensional Hénon map were used. The novelty of the approach is in an effective use of a new type of objective function, which is essential for the whole optimization process of higher periodic orbits as well as an effective use of advanced metaheuristic optimization methods. Although the task of stabilizing the Lozi and Hénon chaotic systems is known, its solution presented for periodic orbit four is not trivial. The task of stabilizing the Lozi chaotic systems for period four is a new approach. Furthermore, modern meta-heuristics were used for own design of the external disturbance sequences. The used optimization methods are a naive grid-based algorithm (NG), a grid-based Nelder-Mead Algorithm (NM), a Genetic Algorithm (GA) as well as Genetic Programming (GP). A connection of GP and second level optimization using GA displays significantly better results than the given stand-alone meta-heuristic techniques.
Keywords
Chaos control, Evolutionary computation, Lozi map, Henon map, Optimization
Authors
MATOUŠEK, R.; LOZI, R.; HŮLKA, T.
Released
9. 8. 2021
Publisher
IEEE
Location
Kraków, Poland
ISBN
978-1-7281-8393-0
Book
2021 IEEE Congress on Evolutionary Computation (CEC)
Edition
IEEE Congress on Evolutionary Computation (CEC)
Edition number
1
Pages from
572
Pages to
579
Pages count
8
URL
https://ieeexplore.ieee.org/document/9504798
BibTex
@inproceedings{BUT172508, author="MATOUŠEK, R. and LOZI, R. and HŮLKA, T.", title="Stabilization of Higher Periodic Orbits of the Lozi and Hénon Maps using Meta-evolutionary Approaches", booktitle="2021 IEEE Congress on Evolutionary Computation (CEC)", year="2021", series="IEEE Congress on Evolutionary Computation (CEC)", number="1", pages="572--579", publisher="IEEE", address="Kraków, Poland", doi="10.1109/CEC45853.2021.9504798", isbn="978-1-7281-8393-0", url="https://ieeexplore.ieee.org/document/9504798" }