Robust multi-objective optimisation in electromagnetic design

In electromagnetic design, optimisation often involves evaluating the finite element method (FEM) – repetitive evaluation of the objective function may require hours or days of computation, making the use of standard direct search methods (e.g. genetic algorithm and particle swarm) impractical. Surrogate modelling techniques are helpful tools in these scenarios. Indeed, their applications can be found in many aspects of engineering design in which a computationally expensive model is involved.Kriging, one of the most widely used surrogate modelling techniques, has become an increasingly active research subject in recent decades. This thesis focuses on four interesting research topics in surrogate-based optimisation: infill sampling efficiency, robust optimisation, and the memory problem encountered in large datasets and multi-objective optimisation. This thesis briefly provides relevant background information and introduces a number of independent novel approaches for each topic, with the aim of increasing efficiency of optimisation process and ability to handle larger datasets