Applying estimation models to accelerate genetic algorithms for charging scheduling problems in wireless rechargeable sensor networks

Jingjing Chen; Hong wei Chen; Wu Yu Chang

doi:10.1117/12.2549450

27 November 2019 Applying estimation models to accelerate genetic algorithms for charging scheduling problems in wireless rechargeable sensor networks

Jingjing Chen, Hong wei Chen, Wu Yu Chang

Author Affiliations +

Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 113211M (2019) https://doi.org/10.1117/12.2549450
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China

Abstract

In this paper, we designed a genetic algorithm based on a new charging path estimation model to obtain an efficient charging scheduling in a wireless rechargeable sensor network. Specifically, we first proposed a charging path estimation model, through which an expected cost of a scheduling charging path can be obtained. Based on this model, a genetic algorithm, which includes a traditional design of chromosome structure, selection, cross-over and mutation operation, supporting the charging scheduling for wireless charging vehicles is devised at the same time. We finally evaluate the performance of the proposed algorithm by extensive simulations. Simulation results show that the proposed algorithm is promising, can improve the performance of wireless rechargeable sensor network.

Citation Download Citation

Jingjing Chen, Hong wei Chen, and Wu Yu Chang "Applying estimation models to accelerate genetic algorithms for charging scheduling problems in wireless rechargeable sensor networks", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113211M (27 November 2019); https://doi.org/10.1117/12.2549450

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