Open AccessModified Parallel Tunicate Swarm Algorithm and Application in 3D WSNs Coverage Optimization
Author(s) -
Jianpo Li Jianpo Li,
Geng-Chen Li Jianpo Li,
Shu-Chuan Chu Geng-Chen Li,
Min Gao Shu-Chuan Chu,
Jeng-Shyang Pan Min Gao
Publication year - 2022
Publication title -
wǎngjì wǎnglù jìshù xuékān
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.231
H-Index - 22
eISSN - 2079-4029
pISSN - 1607-9264
DOI - 10.53106/160792642022032302004
Subject(s) - computer science , benchmark (surveying) , particle swarm optimization , convergence (economics) , wireless sensor network , swarm behaviour , node (physics) , algorithm , scheme (mathematics) , mathematical optimization , computer network , artificial intelligence , mathematics , mathematical analysis , geodesy , structural engineering , geography , engineering , economics , economic growth
As the application of Wireless Sensor Networks (WSNs) in today’s society becomes more and more extensive, and the status is getting higher and higher, the node layout of sensors has also begun to attract social attention. In reality, the coverage of WSNs in 3D space is particularly important. Therefore, it is worth investigating an efficient way to find out the maximum coverage of WSNs. In this paper, a Modified Parallel Tunicate Swarm Algorithm (MPTSA) is proposed based on modified parallelism, which can improve the convergence of the algorithm and optimal global solution. Next, the proposed MPTSA is implemented and tested on 23 benchmark functions to verify the algorithm performance. Finally, a WSNs network layout scheme based on MPTSA is proposed to improve the coverage of the whole network. Experimental results show that, compared with the traditional PSO (Particle Swarm Optimization), improved PSO (PPSO and APSO), GBMO (Gases Brownian Motion Optimization) and traditional TSA, MPTSA family algorithms show better performance in WSNs network layout.