Wang, J.; Zhu, Z.; Zhang, F.; Liu, Y. An Improved Salp Swarm Algorithm for Solving Node Coverage Optimization Problem in WSN. Peer-to-Peer Networking and Applications 2024, doi:10.1007/s12083-024-01637-7.
Wang, J.; Zhu, Z.; Zhang, F.; Liu, Y. An Improved Salp Swarm Algorithm for Solving Node Coverage Optimization Problem in WSN. Peer-to-Peer Networking and Applications 2024, doi:10.1007/s12083-024-01637-7.
Wang, J.; Zhu, Z.; Zhang, F.; Liu, Y. An Improved Salp Swarm Algorithm for Solving Node Coverage Optimization Problem in WSN. Peer-to-Peer Networking and Applications 2024, doi:10.1007/s12083-024-01637-7.
Wang, J.; Zhu, Z.; Zhang, F.; Liu, Y. An Improved Salp Swarm Algorithm for Solving Node Coverage Optimization Problem in WSN. Peer-to-Peer Networking and Applications 2024, doi:10.1007/s12083-024-01637-7.
Abstract
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.
Computer Science and Mathematics, Data Structures, Algorithms and Complexity
Copyright:
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