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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Bibliographic Details
Published inJournal of function spaces Vol. 2022; pp. 1 - 9
Main Authors Zhang, Zhicheng, Zhang, Yan
Format Journal Article
LanguageEnglish
Published New York Hindawi 20.07.2022
John Wiley & Sons, Inc
Wiley
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ISSN2314-8896
2314-8888
2314-8888
DOI10.1155/2022/6983242

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Summary: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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ISSN:2314-8896
2314-8888
2314-8888
DOI:10.1155/2022/6983242