Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic Algorithm
In order to find a chaotic trajectory sequence with strong global optimization ability to help the genetic selection of direction after the reversal of chemotaxis, an improved genetic algorithm based on chaos optimization is proposed by combining the characteristics of chaotic motion with the improv...
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| Published in | Journal of function spaces Vol. 2022; pp. 1 - 9 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Hindawi
20.07.2022
John Wiley & Sons, Inc Wiley |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2314-8896 2314-8888 2314-8888 |
| DOI | 10.1155/2022/6983242 |
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| Abstract | In order to find a chaotic trajectory sequence with strong global optimization ability to help the genetic selection of direction after the reversal of chemotaxis, an improved genetic algorithm based on chaos optimization is proposed by combining the characteristics of chaotic motion with the improved genetic algorithm. The optimal coverage problem in sensor networks can carry out fine optimization search on local areas. The results show that the overall trend of fitness and optimization efficiency is relatively stable. The optimization efficiency will be gradually improved with the continuous progress of time and genetics, and the error analysis will be reduced. This will greatly improve the impact of various adverse factors in the optimization process. In addition, the change rate of fitness is basically kept at a high change rate, which also reflects that the basic framework of the model is very excellent, and the whole algorithm structure and data processing are improved by 54%. The improved genetic algorithm proposed in this paper is used to adjust and optimize the controller parameters. When the uncertain parameters change greatly, the control system still has good control quality and strong robustness. |
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| AbstractList | In order to find a chaotic trajectory sequence with strong global optimization ability to help the genetic selection of direction after the reversal of chemotaxis, an improved genetic algorithm based on chaos optimization is proposed by combining the characteristics of chaotic motion with the improved genetic algorithm. The optimal coverage problem in sensor networks can carry out fine optimization search on local areas. The results show that the overall trend of fitness and optimization efficiency is relatively stable. The optimization efficiency will be gradually improved with the continuous progress of time and genetics, and the error analysis will be reduced. This will greatly improve the impact of various adverse factors in the optimization process. In addition, the change rate of fitness is basically kept at a high change rate, which also reflects that the basic framework of the model is very excellent, and the whole algorithm structure and data processing are improved by 54%. The improved genetic algorithm proposed in this paper is used to adjust and optimize the controller parameters. When the uncertain parameters change greatly, the control system still has good control quality and strong robustness. |
| Audience | Academic |
| Author | Zhang, Zhicheng Zhang, Yan |
| Author_xml | – sequence: 1 givenname: Zhicheng orcidid: 0000-0001-8173-269X surname: Zhang fullname: Zhang, Zhicheng organization: School of ScienceHenan Institute of TechnologyXinxiangHenan 453003Chinahaust.edu.cn – sequence: 2 givenname: Yan orcidid: 0000-0001-5707-8069 surname: Zhang fullname: Zhang, Yan organization: College of Computer and Information EngineeringHenan Normal UniversityXinxiangHenan 453007Chinahenannu.edu.cn |
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| Cites_doi | 10.3390/en15031181 10.3390/w11091795 10.3390/app10020487 10.1007/s11042-018-6477-4 10.11591/ijece.v11i5.pp3903-3912 10.1016/j.cirpj.2021.03.006 10.1007/s11280-019-00705-w 10.1007/s13198-021-01426-y 10.1007/s42524-019-0030-7 10.2112/JCR-SI104-066.1 10.1007/s12666-018-1479-5 |
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| Copyright | Copyright © 2022 Zhicheng Zhang and Yan Zhang. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Zhicheng Zhang and Yan Zhang. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| References | 23 C. Gao (9) 2019; 72 27 X. Luo (1) 2021; 10 T. Tan (14) 2021; 42 C. Yu (19) 2021; 45 J. Chen (24) 2021 A. Mellit (25) 2020; 10 W. Wang (16) 2020; 23 T. Qihua (4) 2018; 35 V. Benifla (6) 2022; 15 J. Hu (22) 2019; 6 Z. Gao (11) 2019; 6 L. Ying (20) 2020; 16 Z. Tao (12) 2020; 11 F. Zepeng (26) 2022; 35 B. He (7) 2018; 40 H. Jin (2) 2020; 79 N. Chen (21) 2019; 11 C. Qingan (17) 2021; 41 L. Xiang (8) 2018; 40 S. Jingfeng (15) 2022; 43 3 S. Liangshan (10) 2020; 56 5 Z. Jing (18) 2022; 42 L. Tao (13) 2022; 45 |
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| SubjectTerms | Algorithms Analysis Applied mathematics Big Data Chemotaxis Control systems Controllers Data processing Error analysis Evolution & development Fitness Fuzzy logic Fuzzy sets Genetic algorithms Genetics Global optimization Learning Linear programming Mathematical models Methods Mutation Optimization algorithms Parameter uncertainty Population Reproductive fitness Robust control |
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| Title | Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic Algorithm |
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