Domain-as-Particle with PSO Methods for Neural-Network Feature Weighting

We present a framework that integrates Particle Swarm Optimization (PSO), machine learning, K-Fold crossvalidation, and surrogate modeling to identify optimal weight vectors for feature scaling in neural network training.In our approach, the n-dimensional weight space is partitioned into nonoverlapping subdomains, each corresponding to a PSO particle.Particle movement is guided by a characteristic vector, which is determined by the best-performing candidates in each subdomain and by information exchanged with neighboring regions.To reduce evaluation costs, a surrogate model-trained on a uniformly sampled subset of candidates-pre-filters particles before full K-Fold validation.The top candidates then undergo comprehensive validation, updating the characteristic vectors for subsequent iterations.This domain-as-particle PSO framework enables efficient weight discovery, significantly reducing computational overhead while maintaining robust performance.The effectiveness of this approach is demonstrated on real-world datasets.