A Novel Swarm Intelligence-Based Optimization Framework for Machine Learning Models

Abstract

The surge of data-driven applications has posed a great challenge to creating effective and accurate machine learning models that can operate with complex and high-dimensional data and dynamic data. Conventional methods of optimization are usually limited in exploration, convergence early and have high computational costs that can adversely impact model performance. This study hypothesizes a new swarm intelligence-based optimization of machine learning models to enhance accuracy of the models, convergence efficiency, and generalization. The suggested framework is based on the principles of collective intelligence by swarm-based algorithms, which are motivated by natural processes which include cooperation, communication, and adaptive decision-making in order to optimize model parameters, feature selection and learning processes. The approach developed combines high-level swarm intelligence systems and machine learning algorithms to produce an efficient balance between the global exploration and the local exploitation. The framework is tested with benchmark datasets of various application fields, taking into account various performance measures, such as accuracy, precision, recall, F1-score, convergence rate, computational efficiency, and robustness. The experimental findings prove the developed swarm intelligence-based optimization framework can significantly enhance predictive behavior and minimize model complexity and optimization errors in comparison with traditional optimization methods.