Ant Colony-Partial Least Squares Algorithm (ACO_PLS)

The Ant Colony-Partial Least Squares Algorithm (ACO_PLS) is an intelligent computational method that integrates Ant Colony Optimization (ACO) with Partial Least Squares Regression (PLS), particularly suitable for variable selection in high-dimensional data analysis. While traditional PLS may include redundant or irrelevant variables during modeling, ACO_PLS leverages the swarm intelligence characteristics of ant colony algorithms to more accurately identify key variables, thereby enhancing model interpretability and predictive performance.

Core Methodology: Ant Colony Optimization Phase: Transforms variable selection into a path search problem where artificial ants "crawl" through the variable space depositing pheromones. Variable combinations with higher pheromone concentrations are more likely to be selected by subsequent ants. PLS Modeling Phase: Performs partial least squares regression on variable subsets selected by the ant colony, evaluates model quality through cross-validation, and provides feedback to the ant colony algorithm for pheromone updates.

Advantages: Ideal for scenarios where the number of variables significantly exceeds sample size (e.g., spectral data, genomic data). Eliminates subjectivity in manual variable selection through adaptive search for optimal solutions. MATLAB implementation efficiently handles matrix operations, with parallel computing capabilities to accelerate ant colony search processes using functions like parfor loops and optimized matrix computations.

Application Extensions: The algorithm can be adapted for additional optimization objectives (e.g., model robustness) or integrated with other dimensionality reduction methods (such as Principal Component Analysis). It is applicable to high-dimensional data analysis in fields including chemometrics and financial forecasting, with potential extensions through custom objective functions and hybrid algorithm implementations.