A multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization problems

•A new multi-fidelity surrogate-model-based optimization framework is proposed to improve reliability and efficiency of existing frameworks.•A data mining method is proposed to address discrepancies between simulation models of different fidelities in the context of global optimization.•A new multi-fidelity surrogate-model-based optimization method is proposed for engineering optimization problems with quite long simulation time per candidate design, whose advantages are verified by mathematical benchmark and real-world problems.

Integrating data-driven surrogate models and simulation models of different accuracies (or fidelities) in a single algorithm to address computationally expensive global optimization problems has recently attracted considerable attention. However, handling discrepancies between simulation models with multiple fidelities in global optimization is a major challenge. To address it, the two major contributions of this paper include: (1) development of a new multi-fidelity surrogate-model-based optimization framework, which substantially improves reliability and efficiency of optimization compared to many existing methods, and (2) development of a data mining method to address the discrepancy between the low- and high-fidelity simulation models. A new efficient global optimization method is then proposed, referred to as multi-fidelity Gaussian process and radial basis function-model-assisted memetic differential evolution. Its advantages are verified by mathematical benchmark problems and a real-world antenna design automation problem.