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Hybrid teaching‐learning optimization of wireless sensor networks
Author(s) -
Tsiflikiotis Antonios,
Goudos Sotirios K.,
Karagiannidis George K.
Publication year - 2017
Publication title -
transactions on emerging telecommunications technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.366
H-Index - 47
ISSN - 2161-3915
DOI - 10.1002/ett.3194
Subject(s) - particle swarm optimization , wireless sensor network , computer science , fusion center , mathematical optimization , hybrid algorithm (constraint satisfaction) , optimization algorithm , algorithm , power (physics) , optimization problem , wireless , artificial intelligence , mathematics , probabilistic logic , computer network , cognitive radio , telecommunications , physics , constraint satisfaction , quantum mechanics , constraint logic programming
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.