A self-adaptive genetic algorithm with improved mutation mode based on measurement of population diversity

2018 ◽  
Vol 31 (5) ◽  
pp. 1435-1443 ◽  
Author(s):  
Na Sun ◽  
Yong Lu
2011 ◽  
Vol 239-242 ◽  
pp. 2847-2850
Author(s):  
Gui Rong Dong ◽  
Peng Bing Zhao

In order to solve the shortcomings of current engineering methods for parameters adjustment that can only deal with them according to single requirement of system and can not optimize them in the whole range, as well as the standard genetic algorithm is prone to premature convergence, therefore, an improved PID parameters adjustment method based on self-adaptive genetic algorithm was proposed. This approach enables crossover and mutation probability automatically change along with the fitness value, not only can it maintain the population diversity, but also can ensure the convergence of the algorithm. A comparison of the dynamic response between the traditional PID control and the PID control based on self-adaptive genetic algorithm was made. Simulation results show that the latter has much superiority.


SeMA Journal ◽  
2016 ◽  
Vol 73 (3) ◽  
pp. 261-285 ◽  
Author(s):  
Tapan Kumar Singh

2012 ◽  
Vol 12 (6) ◽  
Author(s):  
Wen-Jiang Xiang ◽  
Zhi-Xiong Zhou ◽  
Dong-Yuan Ge ◽  
Qing-Ying Zhang ◽  
Qing-He Yao

2014 ◽  
Vol 8 (12) ◽  
pp. 965-972 ◽  
Author(s):  
Xiao‐Kun Wei ◽  
Wei Shao ◽  
Cheng Zhang ◽  
Jia‐Lin Li ◽  
Bing‐Zhong Wang

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