Surrogate Model-Assisted Bees Algorithm for Global Optimisation of Microwave Filters

Microwave filter optimisation is an important example of black-box optimisation, where the objective function is unknown and requires full-wave electromagnetic (EM) simulations. This problem is challenging and even computationally intractable for commonly used global optimisation techniques due to the multimodal and computationally expensive nature of its objective function. This chapter proposes the surrogate-model-assisted Bees Algorithm. Gaussian process regression is used to model the unknown objective function and prescreen promising candidates for expensive EM simulations. In this scheme, the Bees algorithm is used to perform a global search and intelligent sampling for surrogate modelling. This method was evaluated on 7 benchmark functions and compared with the standard Bees Algorithm. Mann‒Whitney U tests indicated the statistical significance of the results. A case study involving a microwave dielectric filter demonstrated the significant advantages of using the proposed method in terms of high-quality design and a reduced number of EM simulation-based evaluations.