MicrowaveBees algorithm filterMicrowave filter optimisationOptimisation is anGlobal optimisation important example of black-box optimisationBlack-box Optimisation, where the objective function is unknownMicrowave filter and requires full-wave electromagnetic (EM) simulationsSimulation. This problem is challenging and even computationally intractable for commonly used global optimisationGlobal optimisation techniques due to the multimodal and computationally expensive nature of its objective function. This chapter proposes the surrogate-model-assisted Bees AlgorithmBees algorithm. Gaussian processGaussian process regression is used to model the unknown objective function and prescreen promising candidates for expensive EM simulationsSimulation. In this scheme, the Bees algorithmBees 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 AlgorithmStandard 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 simulationSimulation-based evaluations.