adabc

adabc is an adaptive variant of the Artificial Bee Colony (ABC) optimization algorithm. It refers to a family of algorithms that extend the standard ABC by incorporating adaptive control strategies to adjust search behavior during a run. The classical ABC uses a swarm of artificial bees consisting of employed, onlooker, and scout bees to explore the search space and balance exploration and exploitation. In adabc, parameters such as perturbation scales, neighborhood sizes, or scout limits are adjusted automatically based on feedback from the optimization process, rather than fixed beforehand. Common approaches include self-adaptation of control parameters, adaptive probability of selection, and dynamic adjustment of the limit value to escape stagnation. Variants are proposed to address continuous, discrete, and mixed-integer problems, and to improve convergence speed and robustness. Applications reported include engineering design, structural optimization, resource allocation, scheduling, machine learning hyperparameter tuning, and other optimization tasks. Advantages claimed for adabc include faster convergence, better robustness to local optima, and improved scalability in some problem classes, though results vary by problem characteristics. Limitations include sensitivity to problem representation, additional computational overhead, and the potential for overfitting of adaptive rules to particular domains. adabc is typically discussed within the swarm intelligence and metaheuristic optimization literature as a family of adaptive ABC methods, and is related to the broader ABC framework.