An Artificial Bee Colony algorithm with Machine Learning for Constrained Optimization in HPC

High-Performance Computing (HPC) is a fundamental tool for tackling complex scientific and engineering problems. Optimizing applications for the heterogeneous and massively parallel nature of modern HPC hardware is essential for achieving timely and resource-efficient results. This paper introduces ABC-MLCO, a novel Artificial Bee Colony (ABC) algorithm enhanced with machine learning for constrained optimization in discrete configuration spaces, specifically targeting HPC scenarios with black-box performance metrics. ABC-MLCO extends the original ABC algorithm with mechanisms that define the feasible region, promote escape from local optima, prevent redundant evaluations, and intensify exploration and exploitation. We first evaluate the algorithm on benchmark functions, observing improvements in regret, feasibility rate, and convergence speed over the original ABC. Then, targeting a virtual screening application for drug discovery, ABC-MLCO outperforms well-known state-of-the-art methods in terms of final regret, achieving average improvements up to $93 \%$.