Efficient global optimization of MEMS based on surrogate model assisted evolutionary algorithm

Optimization plays a key role in MEMS design. However, most MEMS design optimization (exploration) methods either depend on ad-hoc analytical / behavioural models or time consuming numerical simulations. Surrogate modeling techniques have been introduced to integrate generality and efficiency, but the number of design variables which can be handled by most existing efficient MEMS design optimization methods is often less than 5. To address the above challenges, a new method, called Adaptive Gaussian Process-Assisted Differential Evolution for MEMS Design Optimization (AGDEMO) is proposed. The key idea is the proposed ON-LINE adaptive surrogate model assisted optimization framework. In particular, AGDEMO performs global optimization of MEMS using numerical simulation and the differential evolution (DE) algorithm, and a Gaussian process surrogate model is constructed ONLINE to predict the results of expensive numerical simulations. AGDEMO is tested by two actuators (both with 9 design variables). Comparisons with state-of-the-art methods verify advantages of AGDEMO in terms of efficiency, optimization capacity and scalability.