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To understand why hEVOSWO is so effective, we must look at the three mathematical principles that govern its optimization process. First, Equation 1 represents the Spider Wasp Optimization (SWO) component. It uses a probabilistic mechanism where, at any given iteration t, a feature j in the population f subscript i, has a time-decaying probability of inheriting the state of the elite solution f subscript e. This allows for aggressive local refinement as the search progresses. Second, Equation 2 introduces the Evolutionary (EVO) diversity. We apply a mutation operator with a dynamic mutation probability p subscript m. By scaling p subscript m inversely with time, we ensure the model maintains global exploration in the early stages and stabilizes as it converges toward the optimal solution. Finally, Equation 3 defines our Adaptive Stochastic Search. We apply Gaussian noise delta superscript t with a decaying standard deviation sigma subscript t. By using this exponential decay, controlled by the initial magnitude sigma subscript 0 and the decay rate lambda, we ensure that our hyperparameter tuning for C and gamma starts broad but becomes incredibly precise as the algorithm matures. This mathematical synergy is what allows our model to find the global optimum without getting trapped in local minima. |
IEEE/ICACT20260103 Slide.13
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