Optimizing XGBoost via mSMAₚlus: A Novel Meta-Heuristic Approach for High-Precision Multiclass Dry Bean Classification

Precise classification of dry bean varieties holds critical importance for agricultural sustainability, food security, and the preservation of seed quality standards. Traditional classification methods rely on human intervention and exhibit significant error rates; this necessitates the use of high-performance machine learning models and effective optimization strategies. This study aims to propose an innovative framework that optimizes the hyperparameters of the Extreme Gradient Boosting model for classifying seven different bean varieties on the Dry Bean Dataset using meta-heuristic algorithms. Within this study, critical parameters of the XGBoost model, such as learning rate, tree depth, and subsampling rates, have been systematically tuned using Slime Mould, Modified SMA (mSMA), mSMAₚlus, Particle Swarm Optimization, and Grey Wolf Optimizer algorithms. The effectiveness of the proposed methods has been comparatively evaluated against commonly used GridSearch and RandomSearch techniques in the literature. The experimental results, assessed using accuracy, F1-score, precision, and recall metrics, reveal that the proposed mSMAₚlus algorithm achieves a peak classification accuracy of 99. 39% and an F1-score of 0. 9939. This marks a clear architectural advancement over baseline frameworks, raising the classification accuracy baseline by approximately 1. 15% compared to traditional GridSearch approaches within a total execution timeline of 507. 55 s.