Notice of Violation of IEEE Publication Principles "A Hybrid ANFIS/ABC-based Online Selective Harmonic Elimination Switching Pattern for Cascaded Multi-level Inverters of Microgrids," by H. R. Baghaee; M. Mirsalim; G. B. Gharehpetian; H. A. Talebi; A. Niknam-Kumle, in the IEEE Transactions on Industrial Electronics After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. This authorship of this paper was revised without providing attribution to, or permission from, the coauthors of an earlier version of the manuscript. Alireza Niknam-Kumle was responsible for removing the original coauthor names and replacing them with new coauthors, and then resubmitting the content in a new manuscript to IEEE Transactions on Industrial Electronics Due to the nature of this violation, reasonable effort should be made to remove all past and future references to this paper. Control of electronically-coupled distributed energy resources and topology/switching strategy of power converters have the essential roles to provide high quality power for microgrids including unbalanced and nonlinear loads. Multi-level inverters (MLIs) are used to act as high quality converters with more conversion efficiency, and less switching-frequency/switching-losses. Nevertheless their nonlinearity, complexity, and solvability features of selective harmonic elimination strategy that has affected its industrial applications, it can provide desirable output waveform by keeping fundamental component in its desired value as well as eliminating/minimizing of low-order harmonics. This paper presents adaptive neuro-fuzzy inference system (ANFIS) as a switching angle estimator for harmonic optimization problem in cascade MLIs, which is trained based on the database including optimal switching angles obtained for inverter with non-equal DC sources by artificial bee colony (ABC) algorithm. The reliable search ability, accuracy and convergence time of ABC have been compared with particle swarm optimization algorithm. Also, performance of ANFIS is compared with artificial neural network based on correlation coefficient and root mean square error. Simulation results, experimental tests and real-time simulations reveals the effectiveness of proposed estimator for online elimination of low-order harmonics of MLIs in microgrids and its superiority over other methods.