Design of a Front-End Amplifier for the Maximum Power Delivery and Required Noise by HBMO with Support Vector Microstrip Model

Abstract

Honey Bee Mating Optimization (HBMO) is a recent swarm-based optimization algorithm to solve highly nonlinear problems, whose based approach combines the powers of simulated annealing, genetic algorithms, and an effective local search heuristic to search for the best possible solution to the problem under investigation within a reasonable computing time. In this work, the HBMO-based design is carried out for a front-end amplifier subject to be a subunit of a radar system in conjunction with a cost effective 3-D SONNET-based Support Vector Regression Machine (SVRM) microstrip model. All the matching microstrip widths, lengths are obtained on a chosen substrate to satisfy the maximum power delivery and the required noise over the required bandwidth of a selected transistor. The proposed HBMO-based design is applied to the design of a typical ultra-wide-band low noise amplifier with NE3512S02 on a substrate of Rogers 4350 for the maximum output power and the noise figure F(f) = 1 dB within the 5-12 GHz using the T-type of microstrip matching circuits. Furthermore, the effectiveness and efficiency of the proposed HBMO based design are manifested by comparing it with the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and the simple HBMO based designs.