Comparison of optimization algorithms for automated method development of gradient profiles.

Optimization algorithms play an important role in method development workflows for gradient elution liquid chromatography. Their effectiveness has not been evaluated for chromatographic method development using standardized comparisons across factors such as sample complexity, chromatographic response functions (CRFs), gradient complexity, and application type. This study compares six optimization algorithms - Bayesian optimization (BO), differential evolution (DE), a genetic algorithm (GA), covariance-matrix adaptation evolution strategy (CMA-ES), random search, and grid search - for the development of gradient elution LC methods.