A simple and efficient honey bee mating optimization approach to performance characterization of a microwave transistor for the maximum power delivery and required noise

Abstract

In this work, a simple, efficient and multi objective Honey Bee Mating Optimization (HBMO) is presented for the performance characterization of a microwave transistor to deliver maximum power to the load with the required noise F req . Thus all the possible compatible {F req ≥ F min , V out = 1, G Tmax } triplets and the corresponding source Z S and load Z L = Z * out (Z S ) terminations can be obtained in the device operation domain of (V DS , I DS and f) without working analytically for the nonlinear performance and stability equations. HBMO is a recently emerging metaheuristic algorithm that combines the powers of the simulated annealing and genetic algorithms. Here, a microwave transistor is chosen as a case study, effectiveness and efficiency of the HBMO are shown by comparing its performance to those of the standard meta-heuristics Genetic and Particle Swarm algorithms and the mean cost results for 10 runs are found to be 0.22, 1.65 and 1.85, respectively, for the comparable execution times. Furthermore, all the numerical results are found to agree with their analytical counterparts obtained using the microwave, linear circuit and noise theories. The Feasible Design Target Space FDTS can be built by the cross relations among all the possible compatible {F req ≥ F min , V out = 1, G Tmax } triplets together their terminations {Z S , Z L = Z * out (Z S )} covering all the possible amplifier designs where both noise figure and output power are at a premium. Copyright

Competitive evolutionary algorithms for building performance database of a microwave transistor

“…Performance database can be built in the 2 stages: (1) First stage is modelling the signal and noise parameters of the transistor as functions of the device operation parameters of (V DS , I DS , f). For this purpose, the artificial intelligence tools such as multilayer perceptron, support vector regression machine and generalized regression neural network can be used with the typical works given in previous studies [1][2][3] (Figure 1); (2) The final stage is to solve the highly nonlinear performance measure equations of the transistor for a predetermined design strategy for the (Source Z S , Load Z L ) terminations using either analytical [4][5][6][7][8] or numerical [9][10][11][12] methods with the Scattering (S-) and Noise (N-) parameters at the chosen operating conditions.…”

Design optimization of a pattern reconfigurable microstrip antenna using differential evolution and 3D EM simulation‐based neural network model

“…Meta‐heuristic algorithms are advance procedure to form a heuristic that can find an efficient solution to a given problem. Examples for these methods are: Methods that inspired from the behavior of animal and microorganism, such as particle swarm optimization, artificial immune systems, and insect colonies like ant or bees which have been used in design optimization process of different microwave stages . DEA is a multi‐dimensional optimization technique from the class of evolutionary algorithm (EA).…”

“…Examples for these methods are: Methods that inspired from the behavior of animal and microorganism, such as particle swarm optimization, artificial immune systems, and insect colonies like ant or bees which have been used in design optimization process of different microwave stages. [46][47][48][49][50][51] DEA is a multi-dimensional optimization technique from the class of evolutionary algorithm (EA). DEA is originated by Kenneth Price and Rainer M. Storn and first publication of idea of this method was published as a technical report in References 52,53.…”

An efficient method to compute simplified noise parameters of analog amplifiers using symbolic and evolutionary approach

“…NF is nothing but the degradation of signal-to-noise ratio (SNR) due to the electronics components expressed in decibels. The NF can be defined as [17][18][19] NF = SNR in SNR out ð2Þ…”