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To finalize our optimization framework, we move to the specific parameter update rules that define how our SVM learns. Equations 4 and 5 represent the core logic for updating the penalty parameter C and the RBF kernel parameter gamma. As you can see, each update is a combination of the current value, a learning coefficient alpha directed toward the elite solution, and the adaptive Gaussian noise delta superscript t we discussed previously. We use a clip function to ensure these parameters stay within their logically defined search spaces between 0.1 and 50 for C, and between 0.0001 and 1 for gamma. This hybridization is the secret to our model¡¯s success. It allows the optimizer to navigate massive, complex solution spaces using the Evolutionary global exploration, while simultaneously zeroing in on the most promising regions using the Spider Wasp local refinement. By the end of our 100 iterations, we are not just guessing the best parameters; we have mathematically converged on them. |
IEEE/ICACT20260103 Slide.14
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