Optimization results suggest the optimal cutting speed should be increased from ~25 m/min to ~100 m/min with growing tool wear. Soepangkat et al. developed an integrated back propagation neural network-particle swarm optimization (BPNN-PSO) method for CF/epoxy drilling. Experiments validated the satisfactory prediction performance of BPNN-PSO with error less than 5% and the microstructural / delamination damages were effectively minimized under the optimal feed rate (79 mm/min) and spindle speed (2933 rpm). Abhishek et al. applied a fuzzy embedded harmony search (HS) algorithm for CF/epoxy drilling optimization. When compared with genetic algorithm (GA), HS showed better efficiency in searching optimal solutions with less computational cost. For the above mentioned studies, by incorporating multi-objective functions into a single scalar fitness function, a single optimal solution which minimizes/maximizes the overall performance can be achieved. However, such optimization results may be biased as it depends highly on the assigned weight factors, which are usually based on expert knowledge or personal preferences . Additionally, the optimization study on machining of CFRTP, particularly CF/PEKK drilling, is very limited.