Optimization of Yield Based on Machine Learning and PF-SOA Algorithm

In the context of yield optimization, this study proposes a novel hybrid optimization model that combines machine learning with the Shark Optimization Algorithm (PF-SOA) to enhance system efficiency and product quality. The model demonstrates high effectiveness in predicting the yield distribution of target products, achieving an accuracy rate close to 100%. A machine learning regression model is utilized to forecast product yields, with an R-squared value exceeding 98%, highlighting its reliability in evaluating product distribution. Additionally, the study investigates the influence of key variables on yield outcomes, identifying the critical factors that significantly affect yield distribution. By integrating polynomial fitting with PF-SOA, the optimal parameter combinations are determined, leading to substantial improvements in both yield and quality. The optimized parameter selection strategy effectively enhances system performance, providing a promising solution to complex optimization problems. The findings indicate that the application of advanced algorithms not only improves yield but also ensures higher-quality outcomes, offering valuable insights for optimizing production processes. This research underscores the potential of combining cutting-edge optimization techniques with machine learning to solve practical challenges in yield optimization. By advancing more efficient and systematic approaches, this work contributes to the development of innovative methods for achieving optimal outcomes in various production scenarios.