Jiaming Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Jiaming Wang has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Zhengli Zhu
College of Information Science and Technology and Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China
2 shared publications - Fuquan Zhang
College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China
1 shared publication - Fuquang Zhang
Department of Information Science and Technology, Nanjing Forestry University, Nanjing, China
1 shared publication - Yanxiong Liu
Department of Information Science and Technology, Nanjing Forestry University, Nanjing, China
1 shared publication - Yanxiong Liu1 shared publication
Latest Publications
- 8 February 2024
- Published by Springer Nature in Peer-to-Peer Networking and Applications
- Vol. 17 (3) , 1091-1102
An improved salp swarm algorithm for solving node coverage optimization problem in WSN
- 8 February 2024
- Published by Springer Nature in Peer-to-Peer Networking and Applications
- Vol. 17 (3) , 1091-1102
An improved salp swarm algorithm for solving node coverage optimization problem in WSN
- Open Access
- 27 April 2023
- Published by MDPI AG in Preprints.org
An Improved Salp Swarm Algorithm for Solving Node Coverage Optimization problem in WSN
To address the problem that it is easy to form coverage blind areas when wireless sensor networks are randomly deployed, an improved coverage optimization algorithm based on improved Salpa swarm Intelligent algorithm (ATSSA) is proposed for wireless sensor networks. Firstly, the pop-ulation is initialized using tent chaotic sequence to enhance the optimization ability of the algo-rithm. Secondly, the T-distribution mutation is added to the update formula of the leaders for improving the ability to jump out of the local optimal value. Finally, an adaptive formula for updating the position of the follower is proposed, which not only guarantees the local searching ability of the algorithm in the late iteration period, but also improves the global searching ability of the algorithm in the early iteration period. The experimental results show that ATSSA algorithm can improve the coverage of the wireless sensor networks and reduce deployment costs com-pared with other algorithms, when it is used in the wireless sensor networks.
- 27 April 2023
- Published by MDPI AG in Preprints.org
Open AccessAn Improved Salp Swarm Algorithm for Solving Node Coverage Optimization problem in WSN
To address the problem that it is easy to form coverage blind areas when wireless sensor networks are randomly deployed, an improved coverage optimization algorithm based on improved Salpa swarm Intelligent algorithm (ATSSA) is proposed for wireless sensor networks. Firstly, the pop-ulation is initialized using tent chaotic sequence to enhance the optimization ability of the algo-rithm. Secondly, the T-distribution mutation is added to the update formula of the leaders for improving the ability to jump out of the local optimal value. Finally, an adaptive formula for updating the position of the follower is proposed, which not only guarantees the local searching ability of the algorithm in the late iteration period, but also improves the global searching ability of the algorithm in the early iteration period. The experimental results show that ATSSA algorithm can improve the coverage of the wireless sensor networks and reduce deployment costs com-pared with other algorithms, when it is used in the wireless sensor networks.