Automated design method for parameters optimization of CMOS analog circuits based on adaptive genetic algorithm

2007 ◽  
Author(s):  
Jianhai Yu ◽  
Zhigang Mao
Keyword(s):  
Genetic Algorithm ◽  
Analog Circuits ◽  
Design Method ◽  
Automated Design ◽  
Parameters Optimization ◽  
Adaptive Genetic Algorithm ◽  
Cmos Analog Circuits

Adaptive Genetic Algorithm Based Parameters Optimization of PI Controller for Battery System in Wind Farm

2014 ◽  
Vol 1055 ◽  
pp. 375-382
Author(s):  
Hui Ren ◽  
Dan Wei ◽  
David Watts ◽  
Jia Qi Fan
Keyword(s):  
Genetic Algorithm ◽  
Wind Farm ◽  
Storage System ◽  
Pi Controller ◽  
Parameters Optimization ◽  
Adaptive Genetic Algorithm ◽  
Trial And Error ◽  
Battery System ◽  
Trial And Error Method ◽  
Error Method

The randomness and intermittence of wind farm real power generation bring challenges to power system operation, and installing battery system for the mitigation of the fluctuation of wind farm output, following the short-term forecasting curve, even adjusting the output according to the operator’s requirement is a possible way to address the problem from the wind farm side. After a review of various storage control strategies for stabilizing the fluctuation of wind power output, the model of battery energy storage system as well as its control strategy is introduced. Adaptive Genetic Algorithm (AGA) is used for the optimization of PI control parameters. Simulation shows the effectiveness of the proposed method. Moreover, comparing with the trial-and-error method, the optimization algorithm proposed has the advantage of finding the optimal parameters under the lack of experience on PID control, and combined with trial-and-error method, the difficulties engineer could face on tuning the parameters of PI controller is decreased, which increases the feasibility for parameters of PI controller’s being transplanted to similar applications.

Whole Trajectory Design Optimization of Self-Propelled Artillery Based on Adaptive Genetic Algorithm

2014 ◽  
Vol 971-973 ◽  
pp. 1391-1395
Author(s):  
Fu Qiang Niu ◽  
Cheng Xu ◽  
Xiao Xiao
Keyword(s):  
Genetic Algorithm ◽  
Trajectory Optimization ◽  
Optimization Design ◽  
Design Method ◽  
Structural Parameters ◽  
Engineering Application ◽  
The Self ◽  
Charge Weight ◽  
Adaptive Genetic Algorithm ◽  
Trajectory Design

An optimization design method of whole trajectory for a self-propelled artillery, including interior, exterior and terminal ballistic trajectory, is proposed. The structural parameters of the projectile, ballistic parameters, charge weight and explosive weight are selected as the design variables. The optimization objectives are the largest lethal area, the longest range at 45° and the highest utilization of propellant energy. Constraining the maximum bore pressure, muzzle pressure, packing density and the relative position of propellant burning end, the whole trajectory optimization model of the self-propelled is established. The model is calculated using both genetic algorithm and adaptive genetic algorithm. Optimization results show that the effect and convergence rate of the adaptive genetic algorithm are better than those of the genetic algorithm. The method in this paper play an important role in theory reference and engineering application for the whole trajectory design of the self-propelled artillery.

scholarly journals Even Dispersion Design for a Compact Linear Loudspeaker Array with Adaptive Genetic Algorithm

2019 ◽  
Vol 10 (1) ◽  
pp. 227
Author(s):  
Juanjuan Cai ◽  
Yongqiang Pang ◽  
Hui Wang ◽  
Yutian Wang
Keyword(s):  
Genetic Algorithm ◽  
Frequency Band ◽  
Filter Bank ◽  
Sound Pressure ◽  
Pressure Level ◽  
Parameters Optimization ◽  
Adaptive Genetic Algorithm ◽  
Linear Phase ◽  
Radiation Characteristics ◽  
Sound Reinforcement

Even dispersion is important for live sound reinforcement systems; however, it needs to be carefully designed when using a linear loudspeaker array. This is because the audience area is often large, while the loudspeakers are placed centrally in this case for convenience, and thus both the level and the frequency balance may not remain reasonably constant for all audiences. To solve this problem, the adaptive genetic algorithm is firstly introduced in the parameters optimization. Secondly, taking the radiation characteristics at different frequencies into account, a linear-phase non-uniform filter bank is proposed to decompose the broad frequency band into several sub-bands. The audio is re-synthesized with the optimized parameters in each frequency band for a linear loudspeaker array. To show the validity of the proposed method, the simulations and the experiments are conducted to demonstrate that the sound pressure level in the audience area is distributed within ± 1.33 dB, ranging from 200 Hz to 4000 Hz.

Simulation of PID Control of Belt Conveyor System in Coal Mine by an Improved Adaptive Genetic Algorithm

2014 ◽  
Vol 614 ◽  
pp. 215-218 ◽  
Author(s):  
Lei Yu ◽  
Xu Long Zhang ◽  
Feng Wang
Keyword(s):  
Genetic Algorithm ◽  
Coal Mine ◽  
Pid Control ◽  
Pid Controller ◽  
Parameters Optimization ◽  
Step Response ◽  
Adaptive Genetic Algorithm ◽  
Belt Conveyor ◽  
Conveyor System ◽  
And Performance

In order to improve the problem of premature and performance of optimization, an improved adaptive genetic algorithm is proposed for parameters optimization of coal mine belt conveyor PID controller by applying the number of iterations to the crossover operation and mutation operation of genetic algorithm. The simulation shows that the step response of the improved algorithm is superior to the traditional adaptive genetic algorithm.

Application of Control Parameters Optimization of CNC Servo System Based on Self-Adaptive Genetic Algorithm

2011 ◽  
Vol 239-242 ◽  
pp. 2847-2850
Author(s):  
Gui Rong Dong ◽  
Peng Bing Zhao
Keyword(s):  
Genetic Algorithm ◽  
Pid Control ◽  
Population Diversity ◽  
Parameters Optimization ◽  
Adaptive Genetic Algorithm ◽  
Fitness Value ◽  
Current Engineering ◽  
Crossover And Mutation ◽  
Standard Genetic Algorithm ◽  
Self Adaptive

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.

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