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.