Computational optimization of the piston bowl geometry for the different combustion regimes of the dual-mode dual-fuel (DMDF) concept through an improved genetic algorithm

2021 ◽  
Vol 246 ◽  
pp. 114658
Author(s):  
Guangfu Xu ◽  
Antonio García ◽  
Ming Jia ◽  
Javier Monsalve-Serrano
Keyword(s):  
Genetic Algorithm ◽  
Dual Fuel ◽  
Improved Genetic Algorithm ◽  
Dual Mode ◽  
Computational Optimization ◽  
Piston Bowl ◽  
Combustion Regimes

Dual-Mode Control MPPT Algorithm Based on Improved Genetic Algorithm and Perturbation and Observation Method

2012 ◽  
Vol 614-615 ◽  
pp. 1367-1371
Author(s):  
Yu Hong Zhao ◽  
Ze Guang Su ◽  
Zu Hua Xu
Keyword(s):  
Genetic Algorithm ◽  
Photovoltaic Cells ◽  
Maximum Power ◽  
Improved Genetic Algorithm ◽  
Tracking Accuracy ◽  
Dual Mode ◽  
Maximum Power Point ◽  
Mode Control ◽  
Power Point ◽  
Observation Method

To improve the tracking accuracy of the maximum power point of photovoltaic cells can improve the operational efficiency of photovoltaic power generation system and reduce the cost of photovoltaic electricity. Because the common perturbation and observation (P&O) runs at a low speed, this paper proposes a new dual-mode control MPPT algorithm based on improved genetic algorithm and perturbation & observation method. Then this method was used in the experiment. From the experimental results can be seen, the algorithm overcomes the shortcomings of the traditional perturbation and observation method can quickly and accurately tracking the maximum power point of photovoltaic cells output, you can improve system output power.

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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