Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
The hyperspherical acceleration effect particle swarm optimizer
Applied Soft Computing
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Neural Networks
New inspirations in swarm intelligence: a survey
International Journal of Bio-Inspired Computation
Job Shop Scheduling with the Best-so-far ABC
Engineering Applications of Artificial Intelligence
An efficient and robust artificial bee colony algorithm for numerical optimization
Computers and Operations Research
Artificial bee colony algorithm and pattern search hybridized for global optimization
Applied Soft Computing
Block matching algorithm for motion estimation based on Artificial Bee Colony (ABC)
Applied Soft Computing
Enhancing different phases of artificial bee colony for continuous global optimisation problems
International Journal of Advanced Intelligence Paradigms
Artificial bee colony algorithm: a survey
International Journal of Advanced Intelligence Paradigms
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This paper presents an application of swarm intelligence technique namely artificial bee colony (ABC) to extract the small signal equivalent circuit model parameters of GaAs metal extended semiconductor field effect transistor (MESFET) device and compares its performance with particle swarm optimization (PSO) algorithm. Parameter extraction in MESFET process involves minimizing the error, which is measured as the difference between modeled and measured S parameter over a broad frequency range. This error surface is viewed as a multi-modal error surface and robust optimization algorithms are required to solve this kind of problem. This paper proposes an ABC algorithm that simulates the foraging behavior of honey bee swarm for model parameter extraction. The performance comparison of both the algorithms (ABC and PSO) are compared with respect to computational time and the quality of solutions (QoS). The simulation results illustrate that these techniques extract accurately the 16-element small signal model parameters of MESFET. The efficiency of this approach is demonstrated by a good fit between the measured and modeled S-parameter data over a frequency range of 0.5-25GHz.