scholarly journals A Gaussian Estimation of Distribution Algorithm With Random Walk Strategies and Its Application in Optimal Missile Guidance Handover for Multi-UCAV in Over-the-Horizon Air Combat

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 43298-43317 ◽  
Author(s):  
Xiaofei Wang ◽  
Hui Zhao ◽  
Tong Han ◽  
Zhenglei Wei ◽  
Yajun Liang ◽  
...  
Keyword(s):  
Random Walk ◽  
Estimation Of Distribution Algorithm ◽  
Missile Guidance ◽  
Air Combat ◽  
Estimation Of Distribution ◽  
Gaussian Estimation

Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction With Archive

2020 ◽  
Vol 50 (1) ◽  
pp. 140-152 ◽  
Author(s):  
Yongsheng Liang ◽  
Zhigang Ren ◽  
Xianghua Yao ◽  
Zuren Feng ◽  
An Chen ◽  
...  
Keyword(s):  
Estimation Of Distribution Algorithm ◽  
Estimation Of Distribution ◽  
Gaussian Estimation

scholarly journals A Fast Elitism Gaussian Estimation of Distribution Algorithm and Application for PID Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Qingyang Xu ◽  
Chengjin Zhang ◽  
Li Zhang
Keyword(s):  
Probability Model ◽  
Learning Rule ◽  
Estimation Of Distribution Algorithm ◽  
Model Learning ◽  
One Dimensional ◽  
Fast Learning ◽  
Higher Dimensional ◽  
Intelligent Optimization Algorithm ◽  
Estimation Of Distribution ◽  
Gaussian Estimation

Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA.

Anisotropic adaptive variance scaling for Gaussian estimation of distribution algorithm

2018 ◽  
Vol 146 ◽  
pp. 142-151 ◽  
Author(s):  
Zhigang Ren ◽  
Yongsheng Liang ◽  
Lin Wang ◽  
Aimin Zhang ◽  
Bei Pang ◽  
...  
Keyword(s):  
Estimation Of Distribution Algorithm ◽  
Estimation Of Distribution ◽  
Gaussian Estimation
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