Techniques for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a highly effective MCDM method . This method evaluate and find the optimal solution based on the criteria that preference is given to those solutions with the closest Euclidean distance to the ideal solution and largest distance to the negative-ideal solution. TOPSIS is highly computationally efficient and has good comprehensibility in evaluating various alternatives and achieving rational trade-off between criteria . Jadhav et al. proposed an optimization method combining artificial neural network (ANN), GA and TOPSIS for improving the performance of Nimonic C-263 superalloy cryogenic assisted turning. This hybrid approach achieved a prediction accuracy over 93 % and has effectively improved the closeness factor. Li et al. carried out multi-objective optimization of CF/epoxy trimming combining neural network and TOPSIS to minimize surface roughness, tool temperature and tool flank wear. The optimal solutions have effectively improved the closeness coefficient and the prediction accuracy was verified by experiment. Liu et al. deployed a hybrid approach combining GA-BP neural network, NSGA-II and TOPSIS for multi-objective optimization of high speed CF/PEEK milling. The prediction accuracy was over 90% and the optimal milling parameters selected from the Pareto front have effectively eliminated the surface defects such as fibre fracture and matrix smearing. Despite the effectiveness of TOPSIS, hybrid methods integrating multi-objective optimization and TOPSIS is still in its infancy and such hybrid optimization has never been attempted under the context of composite drilling.