Full-wave electromagnetic (EM) simulation, particularly in environments such as CST Studio Suite, makes large-scale antenna optimization computationally prohibitive. We introduce an adaptive surrogate-assisted differential evolution (DE) framework, implemented via a unified CST-Python workflow, designed to accelerate design-on-demand antenna optimization. The workflow integrates target-frequency-driven design requests, multimetric antenna performance targeting, cross-validated surrogate selection, selective CST validation, and iterative CST-verified dataset updating. It begins with Latin hypercube sampling (LHS) to create a CST-simulated training set, selects a regressor (KNN, RF, SVR, GB, XGBoost) via five-fold cross-validation based on mean squared error, and then uses the surrogate to guide the DE search. The core adaptive mechanism involves mandatory full-wave validation of the best design candidate from each optimization cycle, appending the verified result to the training dataset to enable targeted model refinement. Optimization is governed by a multiobjective penalized aggregate function that minimizes the resonant-frequency error while maximizing the design performance metrics of bandwidth, return loss, and gain. We evaluated this approach on three antenna families-dipole (2.00 GHz), microstrip patch (2.55 GHz), and Yagi-Uda (2.50 GHz)-and met targets with only 10-12 full-wave validations per run. Our method achieved a verified design in 9-16 min, whereas pure DE took 21-113 min with 28-55 full-wave solves, and pure PSO took 18-217 min with 28-106 full-wave solves. This corresponds to speedups of 2.38-8.06 & times; and 2.04-13.68 & times;, respectively. This work demonstrates that integrating an adaptively selected surrogate model into the optimization strategy substantially reduces the computational cost of full-wave analysis, establishing a highly efficient and robust methodology for diverse EM design applications.
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