Exploring the Evolution of Artificial Bee Colony Algorithms: Emphasis on Semi-Greedy Strategies

The Artificial Bee Colony (ABC) algorithm has emerged as a prominent metaheuristic technique for solving complex optimization problems due to its simplicity, robustness, and bio-inspired behavior. However, standard ABC suffers from limitations such as slow convergence and premature stagnation. To address these issues, numerous variants have been developed, among which the Semi-Greedy Artificial Bee Colony (SGABC) algorithm introduces a significant advancement by incorporating heuristic-driven yet probabilistic decision strategies. This review provides a comprehensive analysis of the evolution of ABC algorithms, with particular emphasis on semi-greedy strategies. It categorizes key modifications, compares SGABC with standard ABC and other metaheuristics, and highlights its superior performance in problems such as Two-Sided Assembly Line Balancing (2SALB). The paper also explores SGABC’s industrial applications, identifies current research gaps, and proposes future directions including adaptive control, multi-objective frameworks, and real-time optimization. SGABC is positioned as a robust and scalable optimization framework with strong potential for further theoretical development and industrial deployment.