Open AccessResearch on Multi Charging Path Planning for Network Layout
Author(s) -
Shan Huang,
Yi Zuo,
Xiaolu Liu
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2218/1/012013
Subject(s) - genetic algorithm , battery (electricity) , computer science , path (computing) , mathematical optimization , energy consumption , battery capacity , motion planning , energy (signal processing) , wireless sensor network , computer network , artificial intelligence , engineering , electrical engineering , mathematics , machine learning , power (physics) , statistics , physics , quantum mechanics , robot
With the development of Internet of things, Rechargeable Wireless Sensor Network (WRSN) has been widely used. In order to keep the WRSN working, the mobile charger needs to be charged regularly. This paper studies the practical problems of WRSN charging. Aiming at the problem of path and battery capacity planning under complex constraints of multiple chargers, using the traditional MTSP model for reference, the minimum energy consumption path and battery capacity planning model under multiple chargers are established, and the equilibrium factor is creatively designed and applied. Considering the NP-hard characteristics of the problem, an improved genetic algorithm based on Balance degree is designed to solve it. Finally, four cases are tested and compared with the traditional genetic algorithm. The results show that the improved genetic algorithm saves 31.56% of the total distance on average compared with the traditional algorithm, and has better stability and better optimization ability. The results are reasonable and have wide application value.