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We now arrive at the core technical innovation of our work: the Hybrid Evolutionary–Spider Wasp Optimization, or hEVOSWO. The primary objective here is to move beyond simple classification by jointly optimizing two critical factors: the most relevant BiLSTM feature embeddings and the optimal SVM hyperparameters, specifically the penalty parameter C and the RBF kernel parameter gamma.
Our population is initialized with binary vectors where each bit represents a features inclusion, paired with random values for C and gamma. Over 100 iterations, we evaluate the fitness of each individual using a dynamic sample of training data and the F1-score as our primary metric. What makes this hybrid truly powerful is the balance of search strategies. We utilize Evolutionary Optimization (EVO) to introduce diversity through genetic-style perturbations, ensuring we explore the entire search space. Simultaneously, Spider Wasp Optimization (SWO) imposes localized refinements, probabilistically guiding the feature masks toward the elite solution. This dual-layered optimization allows us to achieve a level of precision that standard algorithms simply cannot reach. |
IEEE/ICACT20260103 Slide.12
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