Metaheuristic algorithms are used to find sufficiently good solutions for the optimization problems that are not solvable in a polynomial time. Although metaheuristics offer a general problem-solving framework and can be applied to various types of optimization problems, their performances depend heavily on the problem to be solved. Thus, hybrid metaheuristics are used to combine strong parts of different algorithms. In this study, a novel adaptive metaheuristic selection algorithm is proposed for solving bound-constrained continuous optimization problems. The developed method hybridizes artificial bee colony, differential evolution, and particle swarm optimization at a high level where each algorithm works independently from each other. As a main contribution to the literature, adaptive selection at metaheuristic level among these three algorithms is achieved by using a rank-based credit assignment and UCB1 multiarmed bandit selection. The effectiveness of the developed algorithm has been evaluated on CEC'17 standard benchmark functions. The obtained numerical results indicate that the proposed algorithm outperforms the individual metaheuristics on which it is built and is more effective especially in high dimensional problems. It is also shown that the proposed algorithm is highly comparable with the related algorithms in the literature. Lastly, a case study that achieves adaptive selection of two good-performing algorithms (namely, covariance matrix adaptation evolution strategy and JADE) for the benchmark used in this study supports the effectiveness of the proposed method.