Ant Colony-Optimized Partial Least Squares Algorithm (AOC_PLS)

This article presents a MATLAB-implemented Ant Colony-Optimized Partial Least Squares (AOC_PLS) algorithm designed for variable selection in datasets containing numerous variables. The algorithm efficiently processes large-scale data while preserving critical information throughout the computational process. Key implementation features include leveraging ant colony optimization metaheuristics to identify optimal variable subsets, combined with partial least squares regression for dimensionality reduction and predictive modeling. The algorithm's strength lies in its ability to enhance data structure comprehension, facilitating improved data analysis and prediction accuracy. Through MATLAB's matrix computation capabilities and customized optimization functions, the implementation enables researchers to better understand dataset characteristics and make more informed data-driven decisions. The code structure typically involves pheromone initialization, probabilistic path selection mechanisms, and iterative PLS modeling with cross-validation techniques to ensure robust variable selection.