{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T04:32:55Z","timestamp":1770265975013,"version":"3.49.0"},"reference-count":78,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T00:00:00Z","timestamp":1770163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"FEDER","doi-asserted-by":"publisher","award":["UID-B\/05256\/2020"],"award-info":[{"award-number":["UID-B\/05256\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"FCT","doi-asserted-by":"publisher","award":["UID-P\/05256\/2020"],"award-info":[{"award-number":["UID-P\/05256\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>In injection molding, advanced numerical modeling tools, such as Moldex3D, can significantly improve product development by optimizing part functionality, structural integrity, and material efficiency. However, the complex and nonlinear interdependencies between the several decision variables and objectives, considering the various operational phases, constitute a challenge to the inherent complexity of injection molding processes. This complexity often exceeds the capacity of conventional optimization methods, necessitating more sophisticated analytical approaches. Consequently, this research aims to evaluate the potential of integrating intelligent algorithms, specifically the selection of objectives using Principal Component Analysis and Mutual Information\/Clustering, metamodels using Artificial Neural Networks, and optimization using Multi-Objective Evolutionary Algorithms, to manage and solve complex, real-world injection molding problems effectively. Using surrogate modeling to reduce computational costs, the study systematically investigates multiple methodological approaches, algorithmic configurations, and parameter-tuning strategies to enhance the robustness and reliability of predictive and optimization outcomes. The research results highlight the significant potential of data-mining methodologies, demonstrating their ability to capture and model complex relationships among variables accurately and to optimize conflicting objectives efficiently. In due course, the enhanced capabilities provided by these integrated data-mining techniques result in substantial improvements in mold design, process efficiency, product quality, and overall economic viability within the injection molding industry.<\/jats:p>","DOI":"10.3390\/app16031578","type":"journal-article","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T14:55:11Z","timestamp":1770216911000},"page":"1578","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Surrogate-Assisted Many-Objective Optimization of Injection Molding: Effects of Objective Selection and Sampling Density"],"prefix":"10.3390","volume":"16","author":[{"given":"T.","family":"Marques","sequence":"first","affiliation":[{"name":"Institute for Polymers and Composites, University of Minho, 4704-553 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8899-8380","authenticated-orcid":false,"given":"J. B.","family":"Melo","sequence":"additional","affiliation":[{"name":"Institute for Polymers and Composites, University of Minho, 4704-553 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8964-400X","authenticated-orcid":false,"given":"A. J.","family":"Pontes","sequence":"additional","affiliation":[{"name":"Institute for Polymers and Composites, University of Minho, 4704-553 Guimar\u00e3es, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7777-7625","authenticated-orcid":false,"given":"A.","family":"Gaspar-Cunha","sequence":"additional","affiliation":[{"name":"Institute for Polymers and Composites, University of Minho, 4704-553 Guimar\u00e3es, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,4]]},"reference":[{"key":"ref_1","unstructured":"Goodship, V. (2017). Injection Molding: A Practical Guide, Smithers Rapra."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Menges, G., Rosato, D.V., and Rosato, D.V. (2001). Injection Molding Handbook, Kluwer Academic Publishers.","DOI":"10.1007\/978-1-4615-4597-2"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Osswald, T.A., and Hern\u00e1ndez-Ortiz, J.P. (2006). Polymer Processing: Modeling and Simulation, Hanser, Munich.","DOI":"10.1007\/978-3-446-41286-6"},{"key":"ref_4","unstructured":"Rosato, M.G., and Rosato, D.V. (2012). Injection Molding Handbook, Springer."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gaspar-Cunha, A., Melo, J., Marques, T., and Pontes, A. (2025). A Review on Injection Molding: Conformal Cooling Channels, Modelling, Surrogate Models and Multi-Objective Optimization. Polymers, 17.","DOI":"10.20944\/preprints202503.1555.v1"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"121082","DOI":"10.1016\/j.ijheatmasstransfer.2021.121082","article-title":"Design and fabrication of conformal cooling channels in molds: Review and progress updates","volume":"171","author":"Feng","year":"2021","journal-title":"Int. J. Heat Mass Transf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4291","DOI":"10.1007\/s00170-022-08693-4","article-title":"Design, simulation and optimization of conformal cooling channels in injection molds: A review","volume":"120","author":"Silva","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_8","first-page":"672","article-title":"Additive manufacturing of conformal cooling channels for injection mold tools: Current progress and challenges","volume":"21","author":"Chung","year":"2018","journal-title":"Addit. Manuf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5414","DOI":"10.3934\/mbe.2020292","article-title":"Review of conformal cooling system design and additive manufacturing for injection molds","volume":"17","author":"Wei","year":"2020","journal-title":"Math. Biosci. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wu, T., and Tovar, A. (2018, January 26\u201329). Design for additive manufacturing of conformal cooling channels using thermal-fluid topology optimization and application in injection molds. Proceedings of the ASME Design Engineering Technical Conference 2B-2018, Quebec City, QC, Canada.","DOI":"10.1115\/DETC2018-85511"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"274","DOI":"10.3139\/217.1889","article-title":"Using multi-objective evolutionary algorithms to optimize mechanical properties of injection molded part","volume":"20","author":"Viana","year":"2005","journal-title":"Int. Polym. Process."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gaspar-Cunha, A., Covas, J.A., and Sikora, J. (2022). Optimization of polymer processing: A review (part II-molding technologies). Materials, 15.","DOI":"10.3390\/ma15031138"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"20150202","DOI":"10.1098\/rsta.2015.0202","article-title":"Principal component analysis: A review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1109\/TEVC.2013.2281535","article-title":"An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints","volume":"18","author":"Deb","year":"2014","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1515\/ipp-2024-0174","article-title":"Application of artificial intelligence techniques to select the objectives in the multi-objective optimization of injection molding","volume":"40","author":"Melo","year":"2025","journal-title":"Int. Polym. Process."},{"key":"ref_16","unstructured":"Faes, A.G. (2000). Injection Molding Handbook, Hanser Gardner Publications."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1002\/pen.20198","article-title":"Runner balancing by a direct genetic optimization of shrinkage","volume":"44","author":"Alam","year":"2004","journal-title":"Polym. Eng. Sci."},{"key":"ref_18","unstructured":"Alam, K., and Kamal, M.R. (2003, January 4\u20138). A Genetic optimization of shrinkage by runner balancing. Proceedings of the SPE Annual Technical Conference, Nashville, TN, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1934","DOI":"10.1016\/j.compchemeng.2005.04.005","article-title":"A robust optimization of injection molding runner balancing","volume":"29","author":"Alam","year":"2005","journal-title":"Comput. Chem. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1002\/pen.21652","article-title":"Using multiobjective evolutionary algorithms in the optimization of operating conditions of polymer injection molding","volume":"50","author":"Fernandes","year":"2010","journal-title":"Polym. Eng. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"213","DOI":"10.3139\/217.2511","article-title":"Using multi-objective evolutionary algorithms for optimization of the cooling system in polymer injection molding","volume":"27","author":"Fernandes","year":"2012","journal-title":"Int. Polym. Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/s00170-011-3425-0","article-title":"Multi-objective optimization of MIMO plastic injection molding process conditions based on particle swarm optimization","volume":"58","author":"Xu","year":"2012","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.jmapro.2018.04.007","article-title":"Numerical optimization of process parameters in plastic injection molding for minimizing weldlines and clamping force using conformal cooling channel","volume":"32","author":"Kitayama","year":"2018","journal-title":"J. Manuf. Process"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Nguyen, V.-T., Minh, P.S., Uyen, T.M.T., Do, T.T., Ha, N.C., and Nguyen, V.T.T. (2023). Conformal cooling channel design for improving temperature distribution on the cavity surface in the injection molding process. Polymers, 15.","DOI":"10.3390\/polym15132793"},{"key":"ref_25","first-page":"16","article-title":"Design of conformal cooling layers with self-supporting lattices for additively manufactured tooling","volume":"11","author":"Brooks","year":"2016","journal-title":"Addit. Manuf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s00170-017-0426-7","article-title":"Numerical and experimental evaluation of a conformally cooled H13 steel injection mould manufactured with selective laser melting","volume":"93","author":"Mazur","year":"2017","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_27","first-page":"445","article-title":"Conformal cooling channels in injection molding tools\u2014Design considerations","volume":"12","author":"Tuteski","year":"2018","journal-title":"Int. Sci. Congr. Mach. Technol. Mater."},{"key":"ref_28","first-page":"170","article-title":"Direct metal laser sintering, using conformal cooling, for high volume production tooling","volume":"28","author":"Combrinck","year":"2017","journal-title":"South. Afr. J. Ind. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2957","DOI":"10.1007\/s00170-023-12879-9","article-title":"Machine learning-aided cooling profile prediction in plastic injection molding","volume":"130","author":"Konuskan","year":"2023","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"48","DOI":"10.7862\/tiam.2023.4.6","article-title":"Modular injection mould with a conformal cooling channel for the production of hydraulic filter housings","volume":"122","author":"Zacharski","year":"2023","journal-title":"Technol. I Autom. Monta\u017cu"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1007\/s00170-018-3084-5","article-title":"Automated and robust multi-objective optimal design of thin-walled product injection process based on hybrid RBF-MOGA","volume":"101","author":"Feng","year":"2019","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1007\/s00158-009-0435-8","article-title":"Multidisciplinary optimization of injection molding systems","volume":"41","author":"Ferreira","year":"2010","journal-title":"Struct. Multidiscip. Optim."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1881","DOI":"10.1299\/jamdsm.2020jamdsm0029","article-title":"Simultaneous optimization of variable injection velocity profile and process parameters in plastic injection molding for minimizing weldline and cycle time","volume":"14","author":"Hashimoto","year":"2020","journal-title":"J. Adv. Mech. Des. Syst. Manuf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1016\/j.promfg.2019.06.120","article-title":"Thermo-fluid topology optimization and experimental study of conformal cooling channels for 3D printed plastic injection molds","volume":"34","author":"Jahan","year":"2019","journal-title":"Procedia Manuf."},{"key":"ref_35","first-page":"161","article-title":"Intelligent design optimization of conformal cooling channels for plastic injection molding","volume":"6","author":"Kim","year":"2019","journal-title":"J. Comput. Des. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s00366-008-0120-3","article-title":"Runner sizing in multiple cavity injection mould by non-dominated sorting genetic algorithm","volume":"25","author":"Zhai","year":"2009","journal-title":"Eng. Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1016\/j.matpr.2022.09.555","article-title":"Metal additive manufacturing of conformal cooling channels in plastic injection molds with high number of design variables","volume":"70","author":"Kanbur","year":"2022","journal-title":"Mater. Today Proc."},{"key":"ref_38","unstructured":"Le Goff, R., and Garcia, D. (2011, January 3\u20137). Multi-objective optimization strategy for the design of injection mold cooling system. Proceedings of the Society of Plastics Engineers\u2014EUROTEC 2011 Conference Proceedings, Barcelona, Spain."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1016\/j.promfg.2017.07.078","article-title":"Thermo-mechanical design optimization of conformal cooling channels using design of experiments approach","volume":"10","author":"Jahan","year":"2017","journal-title":"Procedia Manuf."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jahan, S.A., Wu, T., Zhang, Y., El-Mounayri, H., Tovar, A., Zhang, J., Acheson, D., Nalim, R., Guo, X., and Lee, W.H. (2016). Implementation of Conformal Cooling & Topology Optimization in 3D Printed Stainless Steel Porous Structure Injection Molds. Procedia Manufacturing, Elsevier.","DOI":"10.1016\/j.promfg.2016.08.077"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1007\/s00170-024-13754-x","article-title":"Generative design of conformal cooling channels for hybrid-manufactured injection moulding tools","volume":"133","author":"Wilson","year":"2024","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1002\/adv.21554","article-title":"An innovative surrogate-based searching method for reducing warpage and cycle time in injection molding","volume":"35","author":"Zhao","year":"2016","journal-title":"Adv. Polym. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kanbur, B.B., Zhou, Y., Shen, S., and Duan, F. (2020). Neural network-integrated multiobjective optimization of 3D-printed conformal cooling channels. Proceedings of the 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech), Split, Croatia, 23\u201326 September 2020, IEEE Xplore.","DOI":"10.23919\/SpliTech49282.2020.9243730"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"35","DOI":"10.4271\/05-16-01-0004","article-title":"Quality monitoring and multi-objective optimization of the glass fiber-reinforced plastic injection molded products","volume":"16","author":"Liu","year":"2022","journal-title":"SAE Int. J. Mater. Manuf."},{"key":"ref_45","first-page":"107","article-title":"Investigation on conformal cooling system design in injection molding","volume":"8","author":"Hsu","year":"2013","journal-title":"Adv. Prod. Eng. Manag."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s00170-011-3805-5","article-title":"Thermal-structural analysis of bi-metallic conformal cooling for injection moulds","volume":"62","author":"Saifullah","year":"2012","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.matpr.2019.10.020","article-title":"Thermal and mechanical analysis for conformal cooling channel in plastic injection molding","volume":"28","author":"Shen","year":"2019","journal-title":"Mater. Today Proc."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1007\/s10845-021-01841-9","article-title":"Machine learning aided design of conformal cooling channels for injection molding","volume":"34","author":"Gao","year":"2023","journal-title":"J. Intell. Manuf."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.cad.2015.04.004","article-title":"Conformal bubbler cooling for molds by metal deposition process","volume":"69","author":"Wannissorn","year":"2015","journal-title":"CAD Comput. Aided Des."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"7653","DOI":"10.1007\/s13369-019-03855-1","article-title":"Experimental-based multi-objective optimization of injection molding process parameters","volume":"44","author":"Mukras","year":"2019","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2004","journal-title":"Stat. Comput."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chang, H., Sun, Y., Wang, R., and Lu, S. (2023). Application of the NSGA-II algorithm and kriging model to optimise the process parameters for the improvement of the quality of fresnel lenses. Polymers, 15.","DOI":"10.3390\/polym15163403"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.jesit.2017.06.004","article-title":"Correlation based feature selection with clustering for high dimensional data","volume":"5","author":"Chormunge","year":"2018","journal-title":"J. Electr. Syst. Inf. Technol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Forrester, A., Sobester, A., and Keane, A.J. (2008). Engineering Design via Surrogate Modelling: A Practical Guide, Wiley.","DOI":"10.1002\/9780470770801"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Liu, F., Pang, J., and Xu, Z. (2024). Multi-objective optimization of injection molding process parameters for moderately thick plane lens based on PSO-BPNN, OMOPSO, and TOPSIS. Processes, 12.","DOI":"10.3390\/pr12010036"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press.","DOI":"10.1093\/oso\/9780198538493.001.0001"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E., and Williams, C.K.I. (2006). Gaussian Processes for Machine Learning, MIT Press.","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref_59","unstructured":"Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., and Weinberger, K.Q. (2011). Algorithms for Hyper-Parameter Optimization. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1007\/s12204-010-9517-4","article-title":"Multi-objective optimal approach for injection molding based on surrogate model and particle swarm optimization algorithm","volume":"15","author":"Chen","year":"2010","journal-title":"J. Shanghai Jiaotong Univ. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1109\/JPROC.2015.2494218","article-title":"Taking the human out of the loop: A review of Bayesian optimization","volume":"104","author":"Shahriari","year":"2016","journal-title":"Proc. IEEE"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1007\/s00170-019-04488-2","article-title":"Automated multi-objective optimization for thin-walled plastic products using Taguchi, ANOVA, and hybrid ANN-MOGA","volume":"106","author":"Feng","year":"2020","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Kurkin, E., Kishov, E., Barcenas, O.U.E., and Chertykovtseva, V. (2021, January 23\u201325). Gate location optimization of injection molded aerospace brackets using metaheuristic algorithms. Proceedings of the 2021 International Scientific and Technical Engine Conference, Samara, Russian Federation.","DOI":"10.1109\/EC52789.2021.10016812"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"4703","DOI":"10.1007\/s00170-023-12264-6","article-title":"Intelligent recommendation system of injection molding process parameters based on CAE simulation, process window and machine learning","volume":"128","author":"Ma","year":"2023","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1109\/4235.996017","article-title":"A fast and elitist multi-objective genetic algorithm: NSGA-II","volume":"6","author":"Deb","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_66","unstructured":"Kennedy, J., and Eberhart, R. (December, January 27). Particle swarm optimization. In Proceedings of ICNN\u201995\u2014International Conference on Neural Networks, Perth, Australia."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Baruffa, G., Pieressa, A., Sorgato, M., and Lucchetta, G. (2024). Transfer learning-based artificial neural network for predicting weld line occurrence through process simulations and molding trials. J. Manuf. Mater. Process., 8.","DOI":"10.3390\/jmmp8030098"},{"key":"ref_68","unstructured":"Coello, C.A.C., Lamont, G.B., and Van Veldhuizen, D.A. (2007). Evolutionary Algorithms for Solving Multi-Objective Problems, Springer."},{"key":"ref_69","unstructured":"Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms, Wiley."},{"key":"ref_70","unstructured":"Schaffer, J. (1985, January 24\u201326). Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. Proceedings of the 1st International Conference on Genetic Algorithms, Pittsburgh, PA, USA."},{"key":"ref_71","unstructured":"Fonseca, C.M., and Fleming, P.J. (1993, January 17\u201321). Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization. Proceedings of the International Conference on Genetic Algorithms, Urbana-Champaign, IL, USA."},{"key":"ref_72","unstructured":"Zitzler, E., Laumanns, M., and Thiele, L. (2001). SPEA2: Improving the Strength Pareto Evolutionary Algorithm, Computer Engineering and Networks Laboratory (TIK)."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1653","DOI":"10.1016\/j.ejor.2006.08.008","article-title":"SMS-EMOA: Multiobjective selection based on dominated hypervolume","volume":"181","author":"Beume","year":"2007","journal-title":"Eur. J. Oper. Res."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.swevo.2011.05.001","article-title":"Surrogate-assisted evolutionary computation: Recent advances and future challenges","volume":"1","author":"Jin","year":"2011","journal-title":"Swarm Evol. Comput."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guerv\u00f3s, J.J., Bullinaria, J.A., Rowe, J.E., Ti\u0148o, P., Kab\u00e1n, A., and Schwefel, H.-P. (2004). Indicator-Based Selection in Multiobjective Search. Parallel Problem Solving from Nature\u2014PPSN VIII, Springer.","DOI":"10.1007\/b100601"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","article-title":"MOEA\/D: A multiobjective evolutionary algorithm based on decomposition","volume":"11","author":"Zhang","year":"2007","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1007\/s00170-016-8904-x","article-title":"Multi-objective optimization of injection molding process parameters for short cycle time and warpage reduction using conformal cooling channel","volume":"88","author":"Kitayama","year":"2017","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"012117","DOI":"10.1088\/1742-6596\/1637\/1\/012117","article-title":"Multi-objective optimization of injection process parameters based on EBFNN and NSGA-II","volume":"1637","author":"Lu","year":"2020","journal-title":"J. Phys. Conf. Ser."}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/16\/3\/1578\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T14:59:52Z","timestamp":1770217192000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/16\/3\/1578"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,4]]},"references-count":78,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["app16031578"],"URL":"https:\/\/doi.org\/10.3390\/app16031578","relation":{},"ISSN":["2076-3417"],"issn-type":[{"value":"2076-3417","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,4]]}}}