Hybrid teaching-learning optimization of wireless sensor networks

Hybrid teaching-learning optimization of wireless sensor networks. Tsiflikiotis, A., Goudos, S., K., & Karagiannidis, G., K. Transactions on Emerging Telecommunications Technologies, 2017.
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This paper deals with the power allocation of decentralized detection in an optimal wireless sensor network (WSN). The main objective is to find a solution that minimizes the total power consumed by the WSN, so that the error probability at the fusion center would be below a certain threshold. More specifically, we propose a novel stochastic optimization algorithm, called the TLBO-Jaya algorithm, which is a hybrid form of two recently proposed algorithms, ie, the teaching-learning–based optimization (TLBO) and Jaya algorithms. The proposed optimization solution is evaluated for several WSN cases and compared with results from the literature. Additionally, it is compared with both the TLBO and Jaya algorithms, the heat transfer search algorithm, and the popular particle swarm optimization. Numerical results show that the proposed algorithm performs better than other well-known algorithms in almost all tested cases.

@article{
 title = {Hybrid teaching-learning optimization of wireless sensor networks},
 type = {article},
 year = {2017},
 volume = {28},
 id = {15685eb2-8e1e-3d54-8da7-7e10e0a1f4c4},
 created = {2020-02-29T16:57:42.383Z},
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 last_modified = {2023-02-11T18:54:02.494Z},
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 citation_key = {Tsiflikiotis2017},
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 abstract = {This paper deals with the power allocation of decentralized detection in an optimal wireless sensor network (WSN). The main objective is to find a solution that minimizes the total power consumed by the WSN, so that the error probability at the fusion center would be below a certain threshold. More specifically, we propose a novel stochastic optimization algorithm, called the TLBO-Jaya algorithm, which is a hybrid form of two recently proposed algorithms, ie, the teaching-learning–based optimization (TLBO) and Jaya algorithms. The proposed optimization solution is evaluated for several WSN cases and compared with results from the literature. Additionally, it is compared with both the TLBO and Jaya algorithms, the heat transfer search algorithm, and the popular particle swarm optimization. Numerical results show that the proposed algorithm performs better than other well-known algorithms in almost all tested cases.},
 bibtype = {article},
 author = {Tsiflikiotis, Antonios and Goudos, Sotirios K. and Karagiannidis, George K.},
 doi = {10.1002/ett.3194},
 journal = {Transactions on Emerging Telecommunications Technologies},
 number = {11}
}

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