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.

Fifth generation (5G) wireless technology is a promising solution for multi-Gbps data rates in future mobile communications. The new devices are expected to operate at millimeter wave frequencies. To address the 5G requirements novel antennas have to be developed. In this paper the Teaching-Learning-Optimization (TLBO) algorithm is applied in order to design a dual-band E-shaped patch antenna. The geometrical parameters of the aperture-coupled antenna are the inputs of the optimization algorithm. The method gives acceptable design solutions achieving simultaneously S 11 minimization and low VSWR at the frequencies of interest (25GHz and 37GHz).

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Cognitive radio networks are a promising technology for the improvement of the spectrum utilisation. The basic idea is to maximise the utilisation of the available spectrum by dynamically assigning available channels to secondary users. This problem known as the spectrum allocation problem is non-deterministic polynomial-time hard (NP-hard). Chaotic biogeographybased optimisation (CBBO) is a recently proposed evolutionary algorithm that can be applied to the above-mentioned problem. The authors compare CBBO with other popular algorithms in different spectrum allocation problem cases. The results show that CBBO performs in general better or similar to the other algorithms. 8 IET Netw.

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