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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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Function Spaces |
| Online Access: | http://dx.doi.org/10.1155/2022/6983242 |
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| _version_ | 1832555212516098048 |
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| author | Zhicheng Zhang Yan Zhang |
| author_facet | Zhicheng Zhang Yan Zhang |
| author_sort | Zhicheng Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ea074f523c274b55ab8f4d960f05e0ff |
| institution | Kabale University |
| issn | 2314-8888 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Function Spaces |
| spelling | doaj-art-ea074f523c274b55ab8f4d960f05e0ff2025-02-03T05:49:25ZengWileyJournal of Function Spaces2314-88882022-01-01202210.1155/2022/6983242Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic AlgorithmZhicheng Zhang0Yan Zhang1School of ScienceCollege of Computer and Information EngineeringIn 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.http://dx.doi.org/10.1155/2022/6983242 |
| spellingShingle | Zhicheng Zhang Yan Zhang Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic Algorithm Journal of Function Spaces |
| title | Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic Algorithm |
| title_full | Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic Algorithm |
| title_fullStr | Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic Algorithm |
| title_full_unstemmed | Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic Algorithm |
| title_short | Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic Algorithm |
| title_sort | optimization calculation method and mathematical modeling of big data chaotic model based on improved genetic algorithm |
| url | http://dx.doi.org/10.1155/2022/6983242 |
| work_keys_str_mv | AT zhichengzhang optimizationcalculationmethodandmathematicalmodelingofbigdatachaoticmodelbasedonimprovedgeneticalgorithm AT yanzhang optimizationcalculationmethodandmathematicalmodelingofbigdatachaoticmodelbasedonimprovedgeneticalgorithm |