An Improved Crayfish Algorithm‐Based Neural Network Method for Modeling Microstrip Patch Antennas

Artificial neural networks (ANNs) have shown remarkable advantages in antenna modeling and optimization as surrogate models. However, in single-objective modeling, the convergence accuracy of ANN-based antenna models is often insufficient. This paper proposes an improved crayfish optimization algorithm (ICOA) to optimize the initial weights and biases of the neural network, aiming to enhance both convergence accuracy and speed. The improvement incorporates an elite opposition strategy and iterative local search while simplifying certain formulas in the original algorithm. The improved algorithm was evaluated on standard test functions, demonstrating superior convergence speed and accuracy compared to other algorithms. Subsequently, the ICOA was applied to the single-objective modeling of two microstrip patch antennas, predicting antenna return loss S11. The findings indicate that the proposed ICOA-ANN approach demonstrates superior modeling accuracy compared to both the COA-ANN and traditional ANN methods.