*3.3. Model Optimization*

For the prediction of debonding failure, it is known from Section 3.2 that the BP neural network model with the best performance still has the problem of poor generalization ability, so the BP neural network model for debonding failure prediction is considered for optimization. To overcome the shortcomings of the BP algorithm in the generation of weights and thresholds, which are easy to fall into the local optimum, and slow convergence speed, the Dung Beetle Optimizer algorithm (DBO) is used to optimize it. The Dung Beetle Optimizer algorithm is a novel swarm intelligence optimization algorithm proposed in November 2022, mainly inspired by the ball rolling, dancing, foraging, stealing, and reproduction behaviors of dung beetles and the algorithm presents substantially competitive performance with state-of-the-art optimization approaches in terms of the convergence rate, solution accuracy, and stability [85]. The coefficients of variation of the predicted and actual values of the 50 runs of DBO-BP and BP for PE and IC debonding are shown in Figure 8.

**Figure 8.** Comparison of model performance before and after optimization. (**a**) PE debonding; (**b**) IC debonding.

It can be seen from Figure 8 that the overall coefficient of variation of the prediction of debonding failure by the BP neural network model optimized by the DBO algorithm is smaller than that of the BP neural network model. The accuracy of the optimized model is significantly better than that of the traditional BP neural network.
