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Premise and consequent parameters of ANFIS are optimized by an optimization algorithm in its training process. A successful optimization algorithm should be utilized for an effective training process. In this study, an adaptive and hybrid artificial bee colony (aABC) algorithm, which is one of the variants of ABC algorithm, is employed in ANFIS training. aABC algorithm uses arithmetic crossover and adaptive neighborhood radius in the solution generating mechanism. aABC algorithm has gained the ability to obtain fast convergence and quality solution with these two control parameters. ANFIS is trained using aABC algorithm to obtain better solutions according to standard ABC algorithm. Firstly, five nonlinear static test systems are utilized for performance analysis of aABC algorithm. With aABC algorithm, performance increases up to about 16% compared to standard ABC algorithm. At the same time, better convergence is obtained in all examples. Wilcoxon signed rank test is applied to determine significance of the results. In addition, the results reached by aABC algorithm are compared with GA, PSO, HS algorithms and more effective results are found with aABC algorithm. As a result, it is seen that aABC algorithm is more successful than ABC, GA, PSO and HS in ANFIS training for identification of nonlinear static systems. Secondly, ANFIS is also trained by utilizing aABC algorithm for solving a real-world problem. Estimating number of foreign visitors coming to Turkey is selected as a real-world problem. The results obtained are compared standard with standard ABC algorithm, and more successful results are found by aABC algorithm. [ABSTRACT FROM AUTHOR]