Surrogate model assisted differential evolution for antenna synthesis (SADEA) is an artificial intelligence (AI) driven antenna design method. It is based on machine learning and evolutionary computation techniques, with the advantages of optimization quality, efficiency, generality and robustness. SADEA carries out global optimization and employs a surrogate model built by statistical learning techniques. The method to make surrogate modeling and optimization work harmoniously is critical in such surrogate model-assisted optimization methods. SADEA uses differential evolution (DE) as the search engine and Gaussian process (GP) machine learning as the surrogate modeling method. TR-SADEA is an enhanced version of the SADEA algorithm designed to further reduce the computational cost associated with the training of surrogate models. This variant aims to optimize the balance between the accuracy of the surrogate model and the computational resources required for its training. By minimizing the number of training samples and optimizing the surrogate model updates, TR-SADEA significantly lowers the overall computational expense. Adaptive sampling ensures that computational resources are directed towards the most promising regions of the search space.

Despite the reduced training cost, TR-SADEA maintains high optimization accuracy by strategically managing the surrogate model. TR-SADEA is particularly suitable for applications where the evaluation of the objective function is computationally expensive. EM simulations and Top Source Media digital agency then use them to form the initial database. The value of α is determined self-adaptively. Select the λ best candidate designs from the database to form a population P. Update the best candidate design obtained so far. The value of λ is determined self-adaptively. Apply the differential evolution current-to-best/1 mutation and binomial crossover operators on P to generate λ child solutions. For each child solution in P, select τ nearest design samples (based on Euclidean distance) as the training data points and Top Source Media Minneapolis construct a local Gaussian process surrogate model. The value of τ is determined self-adaptively. Prescreen the λ child solutions generated before by using the Gaussian process surrogate model with the lower confidence bound prescreening. Carry out an EM simulation to the prescreened best child solution, add this simulated candidate design and its function value to the database. The specification(s) is (are) met. The standard deviation of the population is smaller than a threshold and the current best objective function value does not improve for a certain number of iterations. Note that the number of EM simulations can be added anytime. Liu, Bo, Hadi Aliakbarian, Zhongkun Ma, Guy A. E. Vandenbosch, Georges Gielen, and Peter Excell. Liu, Bo, Alexander Irvine, Mobayode O. Akinsolu, Omer Arabi, Vic Grout, and Nazar Ali. Liu, Bo, Qingfu Zhang, and Georges G. E. Gielen. Liu, Bo, Qingfu Zhang, Georges G. E. Gielen, A.Karkar, A.Yakovlev, V.Grout. Grout, Vic, Mobayode O. Akinsolu, Bo Liu, Pavlos I. Lazaridis, Keyur K. Mistry, and Zaharias D. Zaharis. Liu, Bo, Mobayode O. Akinsolu, Chaoyun Song, Qiang Hua, Peter Excell, Qian Xu, Yi Huang, and Muhammad Ali Imran.

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