Zobrazeno 1 - 10
of 823
pro vyhledávání: '"parameter adaptation"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-23 (2024)
Abstract In the domain of control engineering, effectively tuning the parameters of proportional-integral-derivative (PID) controllers has persistently posed a challenge. This study proposes a hybrid algorithm (HGJGSO) that combines golden jackal opt
Externí odkaz:
https://doaj.org/article/8cde8c69e926455782be101ae1af2e4a
Autor:
Wu Wenchang
Publikováno v:
Journal of Intelligent Systems, Vol 32, Iss 1, Pp 3944-8 (2023)
This study based on the standard differential evolution (DE) algorithm was carried out to address the issues of control parameter imprinting, mutation process, and crossover process in the standard DE algorithm as well as the issue of multidimensiona
Externí odkaz:
https://doaj.org/article/cf67b8018ba6424c8d40f64f2be950a4
Autor:
Junyuan Zhang, Zhenyu Meng
Publikováno v:
IEEE Access, Vol 11, Pp 88711-88729 (2023)
Differential Evolution(DE) is a widely used technique to tackle complex optimization problems owing to its easy-implementation and excellent performance, nevertheless, the inborn weakness of the crossover operation has not been solved even in the rec
Externí odkaz:
https://doaj.org/article/2651b2b1ca444961b34a1dbab2ebac8a
Autor:
Vladimir Stanovov, Eugene Semenkin
Publikováno v:
Mathematics, Vol 12, Iss 4, p 516 (2024)
Differential evolution is a popular heuristic black-box numerical optimization algorithm which is often used due to its simplicity and efficiency. Parameter adaptation is one of the main directions of study regarding the differential evolution algori
Externí odkaz:
https://doaj.org/article/666cb91beb914d79b07690f3d26dffa1
Publikováno v:
World Electric Vehicle Journal, Vol 15, Iss 1, p 26 (2024)
To enhance the stability and disturbance rejection of wireless charging systems for electric vehicles, we designed a bilateral collaborative control strategy based on BP neural networks, achieving closed-loop constant voltage control for the secondar
Externí odkaz:
https://doaj.org/article/e3610d7c5abf46b1b8a221bbc8c0fb08
Publikováno v:
Axioms, Vol 13, Iss 1, p 59 (2024)
Differential evolution (DE) is one of the most promising black-box numerical optimization methods. However, DE algorithms suffer from the problem of control parameter settings. Various adaptation methods have been proposed, with success history-based
Externí odkaz:
https://doaj.org/article/3c87ec7a4b92484abe7646691848b00b
Akademický článek
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Publikováno v:
Mendel, Vol 29, Iss 1 (2023)
In this paper, the performance of the Differential Evolution algorithm is evaluated when solving real-world problems. A Set of 13 engineering optimisation problems was selected from the fields of mechanics and industry to illustrate the usability of
Externí odkaz:
https://doaj.org/article/3110c3f3b8fa490bb23616f4a0b50018
Publikováno v:
Gong-kuang zidonghua, Vol 48, Iss 5, Pp 58-64 (2022)
In coal mine, the dust concentration is high and the illumination is low. The image acquisition quality and characteristic extraction effect are greatly affected by dust concentration. However, the camera parameters and image processing parameters ca
Externí odkaz:
https://doaj.org/article/65531940983149c68f2ef41607d099e2
Publikováno v:
Complex System Modeling and Simulation, Vol 2, Iss 1, Pp 35-58 (2022)
To address complex single objective global optimization problems, a new Level-Based Learning Differential Evolution (LBLDE) is developed in this study. In this approach, the whole population is sorted from the best to the worst at the beginning of ea
Externí odkaz:
https://doaj.org/article/50dfd3b30e5845948171c4c3fbd310cf