Integrating Particle Swarm Optimization and Quadratic Discriminant Analysis in AI-Driven Software Development for Robust Model Optimization

Abstract

Background Robust optimization models are required in AI-driven applications to provide accurate categorization and high efficiency. Integrating Particle Swarm Optimization (PSO) and Quadratic Discriminant Analysis (QDA) maximizes PSO's optimization power and QDA's classification accuracy, resulting in improved performance in applications that handle complicated, high-dimensional data.
Methods The PSO-QDA hybrid model iteratively optimizes QDA parameters to improve classification bounds and durability. This combination improves computing efficiency, accuracy, and adaptability in artificial intelligence models, making them suited for noisy and imbalanced data situations.
Objectives This study intends to increase classification accuracy, model resilience in high-dimensional datasets, and optimize QDA parameters using PSO for efficient AI-driven software.
Results The PSO-QDA hybrid model achieved 92% optimization accuracy and 89% error reduction, surpassing traditional approaches. Improved computing efficiency and convergence rates revealed the hybrid model's ability to handle high-dimensional, complex datasets.
Conclusion The PSO-QDA hybrid technique enhances classification accuracy, efficiency, and robustness, making it ideal for AI applications in challenging real-world scenarios. The integrated method advances the optimization of complicated AI-driven software.