A Kriging surrogate model assisted Tabu search method for electromagnetic inverse problems

Abstract

To balance the efficiency and accuracy of a global optimization algorithm in solving electromagnetic inverse problems, a Tabu search method assisted by using a Kriging surrogate model is proposed. To reduce the computational time and speed up the algorithm, the Kriging surrogate model is used to predict the objective space. To ensure the accuracy of the final optimal solution, a united trigger is developed to realize dynamically switching between the prediction and the direct objective computation. To utilize the variable space efficiently and provide proper sampling points to update the Kriging surrogate model, an evaluation list is used to evaluate the variable space. A typical mathematical function and electromagnetic inverse problems in low and high frequency are solved to testify the correctness and effectiveness of the proposed method.