Conference proceedings | ©2024
Proceedings of ICDTA 21, Fez, Morocco
Digital Technologies and Applications
Abstract
The inertia weight is one of the inspection parameters that improve the reliability of the Particle Swarm Optimization (PSO). To define this inertia, several techniques have been suggested to improve the quality of the PSO algorithm. In this paper, the aim objective is to determine the new reconfiguration of the radial system by using two strategies: the chaotic descending inertia weight, and the combination strategy of the chaotic inertia weight and the success rate technique aiming to improve the result. To define the reliability and the solidity of these suggested techniques, IEEE 33 bus is used, and the results are compared with other recent studies where they have also used the particle swarm optimization by using other strategies to define the inertia weight parameter. These strategies of inertia weight used in this paper are selected for their features of speed convergence and precision of solution that is near to the optimal solution.
References
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Editors and Affiliations
ENSA, Sidi Mohamed Ben Abdellah University, Fez, Morocco
Saad Motahhir
Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco
Badre Bossoufi
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
M’dioud, M., ELkafazi, I., Bannari, R. (2021). New Reconfiguration of the Radial Distribution Network by Using the Chaotic Mapping and the Success Rate to Improve the PSO Algorithm . In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2021. Lecture Notes in Networks and Systems, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-030-73882-2_1
| Book Title
Digital Technologies and Applications |
Book Subtitle
Proceedings of ICDTA 21, Fez, Morocco |
Editors
Saad Motahhir, Badre Bossoufi |
| Series Title | DOI
https://doi.org/10.1007/978-3-030-73882-2 |
Publisher
Springer Cham
|
| eBook Packages
Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0) |
Copyright Information
The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 |
Softcover ISBN
978-3-030-73881-5 |
| eBook ISBN
978-3-030-73882-2 |
Series ISSN
2367-3370
|
Series E-ISSN
2367-3389
|
| Edition Number
1
|
Number of Pages
XXVIII, 1836
|
Number of Illustrations
295 b/w illustrations, 840 illustrations in colour
|
| Topics
Computational Intelligence, Data Engineering, Artificial Intelligence |