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.

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 Restricted Boltzmann Machine-Assisted Estimation of Distribution Algorithm for Complex Problems

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Lin Bao ◽  
Xiaoyan Sun ◽  
Yang Chen ◽  
Guangyi Man ◽  
Hui Shao
Keyword(s):  
Optimization Problems ◽  
Probability Model ◽  
Computational Cost ◽  
Evolutionary Process ◽  
Estimation Of Distribution Algorithm ◽  
Restricted Boltzmann Machine ◽  
Model Management ◽  
Boltzmann Machine ◽  
Complex Problems ◽  
Estimation Of Distribution

A novel algorithm, called restricted Boltzmann machine-assisted estimation of distribution algorithm, is proposed for solving computationally expensive optimization problems with discrete variables. First, the individuals are evaluated using expensive fitness functions of the complex problems, and some dominant solutions are selected to construct the surrogate model. The restricted Boltzmann machine (RBM) is built and trained with the dominant solutions to implicitly extract the distributed representative information of the decision variables in the promising subset. The visible layer’s probability of the RBM is designed as the sampling probability model of the estimation of distribution algorithm (EDA) and is updated dynamically along with the update of the dominant subsets. Second, according to the energy function of the RBM, a fitness surrogate is developed to approximate the expensive individual fitness evaluations and participates in the evolutionary process to reduce the computational cost. Finally, model management is developed to train and update the RBM model with newly dominant solutions. A comparison of the proposed algorithm with several state-of-the-art surrogate-assisted evolutionary algorithms demonstrates that the proposed algorithm effectively and efficiently solves complex optimization problems with smaller computational cost.

scholarly journals A Hybrid Metaheuristic of Integrating Estimation of Distribution Algorithm with Tabu Search for the Max-Mean Dispersion Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Dieudonné Nijimbere ◽  
Songzheng Zhao ◽  
Haichao Liu ◽  
Bo Peng ◽  
Aijun Zhang
Keyword(s):  
Tabu Search ◽  
Probability Model ◽  
Estimation Of Distribution Algorithm ◽  
Search Procedure ◽  
Hybrid Metaheuristic ◽  
Benchmark Instances ◽  
Conditional Preference ◽  
Estimation Of Distribution ◽  
Dispersion Problem ◽  
Move Operator

This paper presents a hybrid metaheuristic that combines estimation of distribution algorithm with tabu search (EDA-TS) for solving the max-mean dispersion problem. The proposed EDA-TS algorithm essentially alternates between an EDA procedure for search diversification and a tabu search procedure for search intensification. The designed EDA procedure maintains an elite set of high quality solutions, based on which a conditional preference probability model is built for generating new diversified solutions. The tabu search procedure uses a fast 1-flip move operator for solution improvement. Experimental results on benchmark instances with variables ranging from 500 to 5000 disclose that our EDA-TS algorithm competes favorably with state-of-the-art algorithms in the literature. Additional analysis on the parameter sensitivity and the merit of the EDA procedure as well as the search balance between intensification and diversification sheds light on the effectiveness of the algorithm.

scholarly journals Fast Image Segmentation Using Two-Dimensional Otsu Based on Estimation of Distribution Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Wuli Wang ◽  
Liming Duan ◽  
Yong Wang
Keyword(s):  
Image Segmentation ◽  
Fitness Function ◽  
Probability Model ◽  
Computational Cost ◽  
Estimation Of Distribution Algorithm ◽  
Dispersion Matrix ◽  
Two Dimensional ◽  
Guided Filtering ◽  
Estimation Of Distribution ◽  
Mean Filtering

Traditional two-dimensional Otsu algorithm has several drawbacks; that is, the sum of probabilities of target and background is approximate to 1 inaccurately, the details of neighborhood image are not obvious, and the computational cost is high. In order to address these problems, a method of fast image segmentation using two-dimensional Otsu based on estimation of distribution algorithm is proposed. Firstly, in order to enhance the performance of image segmentation, the guided filtering is employed to improve neighborhood image template instead of mean filtering. Additionally, the probabilities of target and background in two-dimensional histogram are exactly calculated to get more accurate threshold. Finally, the trace of the interclass dispersion matrix is taken as the fitness function of estimation of distributed algorithm, and the optimal threshold is obtained by constructing and sampling the probability model. Extensive experimental results demonstrate that our method can effectively preserve details of the target, improve the segmentation precision, and reduce the running time of algorithms.

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

Estimation of Distribution Algorithm using Two Kinds of Information for Solving Knapsack Problem

2014 ◽  
Vol 599-601 ◽  
pp. 1502-1508
Author(s):  
Juan Yu ◽  
Yu Yao He ◽  
Xiao Hua Feng
Keyword(s):  
Knapsack Problem ◽  
Probability Model ◽  
Estimation Of Distribution Algorithm ◽  
Premature Convergence ◽  
Estimation Of Distribution ◽  
Performance Of Algorithm

Former information of probability model and inferior individuals was discarded in the research of estimation of distribution algorithm usually, but they may contain useful information. In this paper, the information of former probability is introduced to avoid premature convergence , and the information of inferior individuals is used to filter generated individuals. The algorithm is used to solve knapsack problem and simulated based on the widely used examples, the results demonstrate the effectiveness of proposed method, the utlilzation of former information of probability model and inferior individuals greatly improve the performance of algorithm .

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