How Artificial Bee Colony Algorithms Have Shaped Scheduling Research: A Systematic Review

This systematic review examines the evolution of Artificial Bee Colony (ABC)-based scheduling research between 2011 and 2025, based on 197 peer-reviewed journal articles systematically identified through a search of the Web of Science Core Collection (up to December 2025). No risk-of-bias assessment was conducted at the study level; coding reliability was confirmed using Cohen’s Kappa (κ = 0.82). The findings were synthesized using bibliometric mapping (VOSviewer), binary logistic regression, and Monte Carlo permutation-based chi-square tests. Hybrid and memetic ABC variants dominate the literature (78.7%), while standalone applications are rare (4.1%). Publication activity has increased significantly since 2020. The likelihood of adopting sustainability-focused objective functions has increased significantly over time (OR = 1.78; 95% CI [1.34; 2.39]; p < 0.001). Learning-based mechanisms, particularly Q-learning adaptations, have emerged as a new phenomenon specific to the period after 2021. The findings indicate that hybridization remains the backbone of the field, while learning-based and sustainability-integrated approaches point toward the future. Two limitations stand out: the heterogeneous “Others/Specialized” category and the limited sample size for the post-2021 period. This research received no external funding and was not registered in a public registry.