  Hyperparameter tuning in machine learning algorithms is a computationally
challenging task due to the large-scale nature of the problem. In order to
develop an efficient strategy for hyper-parameter tuning, one promising
solution is to use swarm intelligence algorithms. Artificial Bee Colony (ABC)
optimization lends itself as a promising and efficient optimization algorithm
for this purpose. However, in some cases, ABC can suffer from a slow
convergence rate or execution time due to the poor initial population of
solutions and expensive objective functions. To address these concerns, a novel
algorithm, OptABC, is proposed to help ABC algorithm in faster convergence
toward a near-optimum solution. OptABC integrates artificial bee colony
algorithm, K-Means clustering, greedy algorithm, and opposition-based learning
strategy for tuning the hyper-parameters of different machine learning models.
OptABC employs these techniques in an attempt to diversify the initial
population, and hence enhance the convergence ability without significantly
decreasing the accuracy. In order to validate the performance of the proposed
method, we compare the results with previous state-of-the-art approaches.
Experimental results demonstrate the effectiveness of the OptABC compared to
existing approaches in the literature.
