A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization | Zendy

Weimin Zheng | Zendy; Ning Liu | Zendy; Qing-Wei Chai | Zendy; ShuChuan Chu | Zendy

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A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization

Author(s) -

Weimin Zheng,

Ning Liu,

Qing-Wei Chai,

ShuChuan Chu

Publication year - 2021

Publication title -

wireless communications and mobile computing

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.42

H-Index - 64

eISSN - 1530-8677

pISSN - 1530-8669

DOI - 10.1155/2021/1676879

Subject(s) - particle swarm optimization , computer science , heuristic , node (physics) , algorithm , multi swarm optimization , position (finance) , wireless sensor network , swarm behaviour , mathematical optimization , real time computing , artificial intelligence , mathematics , computer network , structural engineering , finance , engineering , economics

The mobile sensor network can sense and collect the data information of the monitored object in real time in the monitoring area. However, the collected information is meaningful only if the location of the node is known. This paper mainly optimizes the Monte Carlo Localization (MCL) in mobile sensor positioning technology. In recent years, the rapid development of heuristic algorithms has provided solutions to many complex problems. This paper combines the compact strategy into the adaptive particle swarm algorithm and proposes a compact adaptive particle swarm algorithm (cAPSO). The compact strategy replaces the specific position of each particle by the distribution probability of the particle swarm, which greatly reduces the memory usage. The performance of cAPSO is tested on 28 test functions of CEC2013, and compared with some existing heuristic algorithms, it proves that cAPSO has a better performance. At the same time, cAPSO is applied to MCL technology to improve the accuracy of node localization, and compared with other heuristic algorithms in the accuracy of MCL, the results show that cAPSO has a better performance.

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