Optimization of dual resonance stacked patch resonator by neural hybridized bacteria foraging algorithm

The novel design strategy of a stacked patch resonator working within satellite (X‐Ku) frequency band, through Escherichia coli bacteria‐based optimization technique is introduced. The resonator structure consists of two patches on the two different dielectric sheets and separated by the air‐gap having flexible width. For the implementation of the traditional bacteria foraging optimization algorithm, a objective‐function was needed, which was constructed with the help of back propagation algorithm of artificial neural network. Artificial neural network helps in finding the dual resonance frequency and optimizer in synchronization with neural network provides design variables for the proposed resonator in response to the designer‐specified dual frequency. The response of the hybridized methodology is cross‐evaluated with the help of IE3D simulator and experimentally measured data in the microwave laboratory.