Affiliations: [a]
Jiangxi V&T College of Communications, Nanchang, Jiangxi, China
| [b] School of Computer Engineering, Hubei University of Arts and Science, Xiangyang, Hubei, China
Correspondence:
[*]
Corresponding author. Li Zhu, School of Computer Engineering, Hubei University of Arts and Science, Xiangyang, Hubei, China. E-mail: julyeah@hbuas.edu.cn.
Abstract: Optimization algorithms have been rapidly promoted and applied in many engineering fields, such as system control, artificial intelligence, pattern recognition, computer engineering, etc.; achieving optimization in the production process has an important role in improving production efficiency and efficiency and saving resources. At the same time, the theoretical research of optimization methods also plays an important role in improving the performance of the algorithm, widening the application field of the algorithm, and improving the algorithm system. Based on the above background, the purpose of this paper is to apply the intelligent optimization algorithm based on grid technology platform to research. This article first briefly introduced the grid computing platform and optimization algorithms; then, through the two application examples of the TSP problem and the Hammerstein model recognition problem, the common intelligent optimization algorithms are introduced in detail. Introduction: Algorithm description, algorithm implementation, case analysis, algorithm evaluation and algorithm improvement. This paper also applies the GDE algorithm to solve the reactive power optimization problems of the IEEE14 node, IEEE30 node and IEEE57 node. The experimental results show that the minimum network loss of the three systems obtained by the GDE algorithm is 12.348161, 16.348152, and 23.645213, indicating that the GDE algorithm is an effective algorithm for solving the reactive power optimization problem of power systems.
