Mathematical modeling and simulation of data chaos model optimization calculation process

Yang, Jiangang; Sharma, Parv; Ikbal, Mohammad Asif

doi:10.1007/s13198-021-01426-y

Cited by 1 publication

(1 citation statement)

References 29 publications

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“…Chaotic motion exists widely in nonlinear systems, it has excellent properties such as ergodicity and randomness, and it can go to each state in a certain domain irreproducibly; this feature makes chaotic search inherently random and orbital history. It can ensure that all possible states can be traversed without repetition in the global scope, which is conducive to overcome the limitations of the general random algorithm with distributed traversal as the search mechanism, which will be more conducive to the optimization of the chaotic model of byte big data, and solve the problem [27]. In order to overcome the convergence problem of the basic GA, this paper also adopts the elite selection strategy.…”

Section: Construction Of Mathematical Model and Applicationmentioning

confidence: 99%

Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic Algorithm

Zhang

2022

Journal of Function Spaces

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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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Section: Construction Of Mathematical Model and Applicationmentioning

confidence: 99%

Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic Algorithm

Zhang

2022

Journal of Function Spaces

View full text Add to dashboard Cite

show abstract

Mathematical modeling and simulation of data chaos model optimization calculation process

Cited by 1 publication

References 29 publications

Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic Algorithm

Optimization Calculation Method and Mathematical Modeling of Big Data Chaotic Model Based on Improved Genetic Algorithm

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