Application of Improved Genetic Algorithm in Vehicle Networked Cloud Data Platform

2018 ◽  
Author(s):  
Zeyu Yang ◽  
Zunwen He
Keyword(s):  
Genetic Algorithm ◽  
Improved Genetic Algorithm ◽  
Cloud Data ◽  
Data Platform

scholarly journals Research on Resource Scheduling of Cloud Computing Based on Improved Genetic Algorithm

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Juanzhi Zhang ◽  
Fuli Xiong ◽  
Zhongxing Duan
Keyword(s):  
Genetic Algorithm ◽  
Data Center ◽  
Completion Time ◽  
Resource Scheduling ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Cloud Data Center ◽  
Two Stage ◽  
Cloud Data ◽  
Conventional Genetic Algorithm

In order to solve the problem that the resource scheduling time of cloud data center is too long, this paper analyzes the two-stage resource scheduling mechanism of cloud data center. Aiming at the minimum task completion time, a mathematical model of resource scheduling in cloud data center is established. The two-stage resource scheduling optimization simulation is realized by using the conventional genetic algorithm. On the technology of the conventional genetic algorithm, an adaptive transformation operator is designed to improve the crossover and mutation of the genetic algorithm. The experimental results show that the improved genetic algorithm can significantly reduce the total completion time of the task, and has good convergence and global optimization ability.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

Combining improved genetic algorithm and matrix semi-tensor product (STP) in color image encryption

2021 ◽  
Vol 183 ◽  
pp. 108041
Author(s):  
Xiuli Chai ◽  
Xiangcheng Zhi ◽  
Zhihua Gan ◽  
Yushu Zhang ◽  
Yiran Chen ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Tensor Product ◽  
Image Encryption ◽  
Color Image ◽  
Improved Genetic Algorithm ◽  
Color Image Encryption
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