Abstract—This study investigated the influence of machining parameters and performed a comparative multi-objective optimization of surface roughness (Ra) and Material Removal Rate (MRR) in the milling of 7075 aluminum alloy using Multi-Objective Particle Swarm Optimization (MOPSO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), and Multi-Objective Ant Colony Optimization (MOACO). Three machining parameters, including spindle speed (S), feed rate (f), and depth of cut (d), were considered. Analysis of Variance (ANOVA) results showed that S and f significantly affect Ra (p < 0.05), contributing 20.06% and 79.68%, respectively, while f and d are the most significant factors influencing MRR (p < 0.05), accounting for 45.01% and 40.11% of the total contribution. A predictive model for Ra developed using the Group Method of Data Handling (GMDH) demonstrated high predictive performance, with R² values of 0.9990 and 0.9962 for the training and validation datasets, respectively. Comparative analysis indicated that NSGA-II produced the most stable solutions, whereas SPEA2 and MOACO exhibited less balanced performance, and MOPSO achieved rapid convergence with relatively dispersed solutions. Experimental validation of Ra and analytical verification of MRR confirmed the reliability of the proposed framework, with mean deviations of 6.5% and 0.37%, respectively. Unlike prior investigations that examined individual algorithms or lacked integrated experimental assessment, this study presents a systematic cross-algorithm evaluation under identical machining conditions. The proposed framework integrates statistical contribution analysis, predictive modeling, and experimental validation, thereby establishing a robust and practically applicable approach for multi-objective milling optimization.

Keywords—multi-objective optimization, milling, Multi-Objective Particle Swarm Optimization (MOPSO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multi-Objective Ant Colony Optimization (MOACO)

Cite: Son Hoang, Cong Chi Tran, Van Tinh Pham, and Van Tuong Tran, "Multi-objective Optimization of the 7075 Aluminum Alloy Milling Process: A Comparative Study of MOPSO, NSGA-II, SPEA2, and MOACO," International Journal of Mechanical Engineering and Robotics Research, Vol. 15, No. 2, pp. 211-221, 2026. doi: 10.18178/ijmerr.15.2.211-221

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).