A Combined Middle-Long Load Forecasting Model Base on Differential Evolution Method

2011 ◽  
Vol 181-182 ◽  
pp. 594-598
Author(s):  
Dong Xiao Niu ◽  
Jian Jun Wang ◽  
Li Li
Keyword(s):  
Differential Evolution ◽  
Load Forecasting ◽  
Differential Evolution Algorithm ◽  
Forecasting Model ◽  
Model Base ◽  
Combined Load ◽  
Forecasting Models ◽  
Evolution Algorithm ◽  
Combined Forecasting ◽  
Better Than

Middle-long load forecasting is an important issue for power system’s plan, investment and operation. In this paper, differential evolution algorithm is used to determine the weights of the Combined load forecasting models, which is combined with several single middle-long forecasting models. The experiment results point out that the proposed model’s performance is better than any single forecasting model. It also shows that differential evolution algorithm can choose the weights of combined forecasting model effectually.

Multilevel Image Thresholding Based on Improved Expectation Maximization (EM) and Differential Evolution Algorithm

2021 ◽  
pp. 2150013
Author(s):  
Ehsan Ehsaeyan ◽  
Alireza Zolghadrasli
Keyword(s):  
Em Algorithm ◽  
Differential Evolution ◽  
Expectation Maximization ◽  
Differential Evolution Algorithm ◽  
Gaussian Mixture ◽  
Standard Test ◽  
Computational Time ◽  
Image Thresholding ◽  
Evolution Algorithm ◽  
Better Than

Multilevel image thresholding is an essential step in the image segmentation process. Expectation Maximization (EM) is a powerful technique to find thresholds but is sensitive to the initial points. Differential Evolution (DE) is a robust metaheuristic algorithm that can find thresholds rapidly. However, it may be trapped in the local optimums and premature convergence occurs. In this paper, we incorporate EM algorithm to DE and introduce a novel algorithm called EM+DE which overcomes these shortages and can segment images better than EM and DE algorithms. In the proposed method, EM estimates Gaussian Mixture Model (GMM) coefficients of the histogram and DE tries to provide good volunteer solutions to EM algorithm when EM converges in local areas. Finally, DE fits GMM parameters based on Root Mean Square Error (RMSE) to reach the fittest curve. Ten standard test images and six famous metaheuristic algorithms are considered and result on global fitness. PSNR, SSIM, FSIM criteria and the computational time are given. The experimental results prove that the proposed algorithm outperforms the EM and DE as well as EM+ other natural-inspired algorithms in terms of segmentation criteria.

A Fractal Mutation Factor Modified Differential Evolution Algorithm

2014 ◽  
Vol 598 ◽  
pp. 418-423 ◽  
Author(s):  
Xiao Hong Qiu ◽  
Bo Li ◽  
Zhi Yong Cui ◽  
Jing Li
Keyword(s):  
Brownian Motion ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Hurst Index ◽  
Test Results ◽  
Fractal Brownian Motion ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Better Than

To get better solution by improving the mutation strategy of Differential Evolution algorithm, a fractal mutation strategy is introduced. The fractal mutation factor of the proposed Fractal Mutation factor Differential Evolution (FMDE) algorithm is simulated by fractal Brownian motion with a different Hurst index. The new algorithm is test on 25 benchmark functions presented at 2005 IEEE Congress on Evolutionary Computation (CEC2005). The optimization results of at least 10 benchmark functions are significantly better than the results obtained by JADE and CoDE, and most of the rest of the test results are approximate. This shows that FMDE can significantly improve the accuracy and adaptability to solve optimization problems.

A History-Driven Differential Evolution Algorithm for Optimization in Dynamic Environments

2018 ◽  
Vol 27 (06) ◽  
pp. 1850028
Author(s):  
Zhen Zhu ◽  
Long Chen ◽  
Changgao Xia ◽  
Chaochun Yuan
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Global Optimum ◽  
Trial Vector ◽  
Benchmark Instances ◽  
Evolution Algorithm ◽  
Selection Operator ◽  
New Location ◽  
Better Than

This paper presents a novel differential evolution algorithm to solve dynamic optimization problems. In the proposed algorithm, the entire population is composed of several subpopulations, which are evolved independently and excluded each other by a predefined Euclidian-distance. In each subpopulation, the “DE/best/1” mutation operator is employed to generate a mutant individual in this paper. In order to fully exploit the newly generated individual, the selection operator was extended, in which the newly generated trial vector competed with the worst individual if this trial vector was worse than the target vector in terms of the fitness. Meanwhile, this trial vector was stored as the historical information, if it was better than the worst individual. When the environmental change was detected, some of the stored solutions were retrieved and expected to guide the reinitialized solutions to track the new location of the global optimum as soon as possible. The proposed algorithm was compared with several state-of-the-art dynamic evolutionary algorithms over the representative benchmark instances. The experimental results show that the proposed algorithm outperforms the competitors.

scholarly journals An Enhancing Differential Evolution Algorithm with a Rank-Up Selection: RUSDE

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 569
Author(s):  
Kai Zhang ◽  
Yicheng Yu
Keyword(s):  
Differential Evolution ◽  
Optimization Problem ◽  
Differential Evolution Algorithm ◽  
Iteration Process ◽  
Excellent Performance ◽  
De Algorithm ◽  
Current Population ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Better Than

Recently, the differential evolution (DE) algorithm has been widely used to solve many practical problems. However, DE may suffer from stagnation problems in the iteration process. Thus, we propose an enhancing differential evolution with a rank-up selection, named RUSDE. First, the rank-up individuals in the current population are selected and stored into a new archive; second, a debating mutation strategy is adopted in terms of the updating status of the current population to decide the parent’s selection. Both of the two methods can improve the performance of DE. We conducted numerical experiments based on various functions from CEC 2014, where the results demonstrated excellent performance of this algorithm. Furthermore, this algorithm is applied to the real-world optimization problem of the four-bar linkages, where the results show that the performance of RUSDE is better than other algorithms.

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