Gray-Encoded Hybrid Accelerating Genetic Algorithm for Global Optimization of Water Environmental Model

2005 ◽  
pp. 129-136 ◽  
Author(s):  
Xiaohua Yang ◽  
Zhifeng Yang ◽  
Zhenyao Shen ◽  
Guihua Lu
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Environmental Model ◽  
Accelerating Genetic Algorithm

A Novel Global Optimization Method to Design Laser Parameters with Artificial Neural Network and Genetic Algorithm

2010 ◽  
Vol 37 (5) ◽  
pp. 1203-1208
Author(s):  
肖光宗 Xiao Guangzong ◽  
龙兴武 Long Xingwu ◽  
张斌 Zhang Bin ◽  
吴素勇 Wu Suyong ◽  
赵洪常 Zhao Hongchang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Global Optimization ◽  
Optimization Method ◽  
Global Optimization Method ◽  
Laser Parameters ◽  
Artificial Neural

A study on global optimization and deep neural network modeling method in performance-seeking control

2019 ◽  
Vol 234 (1) ◽  
pp. 46-59 ◽  
Author(s):  
Qiangang Zheng ◽  
Dawei Fu ◽  
Yong Wang ◽  
Haoying Chen ◽  
Haibo Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Global Optimization ◽  
Interval Analysis ◽  
Sequential Quadratic Programming ◽  
Deep Neural Network ◽  
Network Modeling ◽  
Engine Performance ◽  
Neural Network Modeling ◽  
Swarm Optimization

In this article, a novel performance-seeking control method based on deep neural network and interval analysis is proposed to obtain a better engine performance. A deep neural network modeling method which has stronger representation capability than conventional neural network and can deal with big training data is adopted to establish an on-board model in the subsonic and supersonic cruising envelops. Meanwhile, a global optimization algorithm interval analysis is applied here to get a better engine performance. Finally, two simulation experiments are conducted to verify the effectiveness of the proposed methods. One is the on-board model modeling which compares the deep neural network with the conventional neural network, and the other is the performance-seeking control simulations comparing interval analysis with feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm, respectively. These two experiments show that the deep neural network has much higher precision than the conventional neural network and the interval analysis gets much better engine performance than feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm.

scholarly journals Understanding the structural diversity of freestanding Al2O3 ultrathin films through a DFTB-aided genetic algorithm

Nanoscale ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 6153-6163
Author(s):  
Maxime Van den Bossche ◽  
Claudine Noguera ◽  
Jacek Goniakowski
Keyword(s):  
Thin Film ◽  
Genetic Algorithm ◽  
Global Optimization ◽  
Ultrathin Films ◽  
Structural Diversity

Global optimization reveals a host of unexpected Al2O3 thin film structures stabilized by three different microscopic mechanisms.

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