References
- Anderson-Cook, C. M., and L. Lu. 2018. “Graphics to facilitate informative discussion and team decision-making” (with discussions). Applied Stochastic Models in Business and Industry 34 (6):963–80. doi:10.1002/asmb.2325.
- Borkowski, J. 2003. Using a genetic algorithm to generate small exact response surface designs. Saskatchewan, CA: University of Regina.
- Cao, Y., B. J. Smucker, and T. J. Robinson. 2017. A hybrid elitist pareto-based coordinate exchange algorithm for constructing multi-criteria optimal experimental designs. Statistics and Computing 27 (2):423–37. doi:10.1007/s11222-016-9630-9.
- Chapman, J. L., L. Lu, and C. M. Anderson-Cook. 2018. Using multiple criteria optimization and two-stage genetic algorithms to select a population management strategy with optimized reliability. Complexity 2018:1–18. doi:10.1155/2018/7242105.
- Chen, R. B., S. P. Chang, W. Wang, H.-C. Tung, and W. K. Wong. 2015. Minimax optimal designs via particle swarm optimization methods. Statistics and Computing 25 (5):975–88. doi:10.1007/s11222-014-9466-0.
- Chen, P. Y., R. B. Chen, and W. K. Wong. 2022. Particle swarm optimization for searching efficient experimental designs: A review. WIREs Computational Statistics 14 (5):4301–17. doi:10.1002/wics.1578.
- Chen, R. B., Y. W. Hsu, Y. Hung, and W. Wang. 2014. Discrete particle swarm optimization for constructing uniform design on irregular regions. Computational Statistics & Data Analysis 72:282–97. doi:10.1016/j.csda.2013.10.015.
- Chen, R. B., C. H. Li, Y. Hung, and W. Wang. 2019. Optimal noncollapsing space-filling designs for irregular experimental regions. Journal of Computational and Graphical Statistics 28 (1):74–91. doi:10.1080/10618600.2018.1482760.
- Chen, Y., and A. Zhou. 2022. Multiobjective portfolio optimization via Pareto front evolution. Complex & Intelligent Systems 8 (5):4301–17. doi:10.1007/s40747-022-00715-8.
- Clerc, M. Standard particle swarm optimization. Technical report, HAL Achivesouvertes, 2012.
- Clerc, M., and J. Kennedy. Feb 2002. The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6 (1):58–73. doi:10.1109/4235.985692.
- Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6 (2):182–97. doi:10.1109/4235.996017.
- Deb, K., and H. Jain. 2014. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation 18 (4):577–601. doi:10.1109/TEVC.2013.2281535.
- Derringer, G., and R. Suich. 1980. Simultaneous optimization of several response variable. Journal of Quality Technology 12 (4):214–9. doi:10.1080/00224065.1980.11980968.
- Fox, A. D., Corne, D. W. Mayorga Adame, C. G. Polton, J. A. Henry, L.-A. Roberts, and J. M. 2019. An efficient multi-objective optimization methods for use in the design of marine protected area networks. Frontiers in Marine Science 6 (17). doi:https://doi.org/10.3389/fmars.2019.00017.
- Goos, P., and B. Jones. 2011. Optimal design of experiments: A case study approach. Hoboken, NJ: John Wiley and Sons, Ltd.
- Gronwald, W., T. Hohm, and D. Hoffmann. 2008. Evolutionary pareto-optimization of stably folding peptides. BMC Bioinformatics 9:109. doi:10.1186/1471-2105-9-109.
- Hernandez, L. N., and C. J. Nachtsheim. 2018. Fast computation of exact G-optimal designs via Il-optimality. Technometrics 60 (3):297–305. doi:10.1080/00401706.2017.1371080.
- Hernández-Díaz, A. G., L. V. Santana-Quintero, C. A. Coello Coello, and J. Molina. 2007. Pareto-adaptive epsilon-dominance. Evolutionary Computation 15 (4):493–517. doi:10.1162/evco.2007.15.4.493.
- Jensen, W. A. 2018. Open problems and issues in optimal design. Quality Engineering 30 (4):583–93. doi:10.1080/08982112.2018.1517884.
- Laumanns, M., L. Thiele, K. Deb, and E. Zitzler. 2002. Combining convergence and diversity in evolutionary multi-objective optimization. Evolutionary Computation 10 (3):263–82. doi:10.1162/106365602760234108.
- Lin, C. F. D., C. M. Anderson-Cook, M. S. Hamada, L. M. Moore, and R. R. Sitter. 2015. Using genetic algorithms to design experiments: A review. Quality and Reliability Engineering International 31 (2):155–67. doi:10.1002/qre.1591.
- Lu, L., and C. M. Anderson-Cook. 2012. Rethinking the optimal response surface design for a first-order model with two-factor interactions, when protecting against curvature. Quality Engineering 24 (3):404–22. doi:10.1080/08982112.2012.629940.
- Lu, L., and C. M. Anderson-Cook. 2013. Adapting the hypervolume quality indicator to quantify trade-offs and search efficiency for multiple criteria decision-making using pareto fronts. Quality and Reliability Engineering International 29 (8):1117–33. doi:10.1002/qre.1464.
- Lu, L., and C. M. Anderson-Cook. 2014. Balancing multiple criteria incorporating cost using Pareto front optimization for split-plot designed experiments. Quality and Reliability Engineering International 30 (1):37–55. doi:10.1002/qre.1476.
- Lu, L., and C. M. Anderson-Cook. 2021. Input-response space-filling (IRSF) designs. Quality and Reliability Engineering International 37 (8):3529–51. doi:10.1002/qre.2931.
- Lu, L., Anderson-Cook, C. M. Robinson, and T. J. 2011. Optimization of designed experiments based on multiple criteria utilizing a Pareto frontier. Technometrics 53 (4):353–65. doi:10.1198/TECH.2011.10087.
- Lu, L., C. Anderson-Cook, and M. Zhang. 2021. Understanding the merits of winning solutions from a data competition for varied sets of objectives. Statistical Analysis and Data Mining: The ASA Data Science Journal 14 (6):556–74. doi:10.1002/sam.11494.
- Lu, L., C. M. Anderson-Cook, and D. Lin. 2014. Optimal designed experiments using a pareto front search for focused preference of multiple objectives. Computational Statistics & Data Analysis 71:1178–92. doi:10.1016/j.csda.2013.04.008.
- Lu, L., J. L. Chapman, and C. M. Anderson-Cook. 2013. A case study on selecting a best allocation of new data for improving the estimation precision of system and sub-system reliability using Pareto fronts. Technometrics 55 (4):473–87. doi:10.1080/00401706.2013.831776.
- Lu, L., J. L. Chapman, and C. M. Anderson-Cook. 2017. Multiple response optimization for higher dimensions in factors and responses. Quality and Reliability Engineering International 33 (4):727–44. doi:10.1002/qre.2051.
- Lu, L., M. Li, and C. M. Anderson-Cook. 2016. Multiple objective optimization in reliability demonstration tests. Journal of Quality Technology 48 (4):326–42. doi:10.1080/00224065.2016.11918172.
- Lukemire, J., A. Mandal, and W. K. Wong. 2019. d-qpso: A quantum-behaved particle swarm technique for finding D-optimal designs with discrete and continuous factors and a binary response. Technometrics 61 (1):77–87. doi:10.1080/00401706.2018.1439405.
- Mak, S., and V. R. Joseph. 2018. Minimax and minimax projection designs using clustering. Journal of Computational and Graphical Statistics 27 (1):166–78. doi:10.1080/10618600.2017.1302881.
- Marler, R. T., and J. S. Arora. 2004. Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization 26 (6):369–95. doi:10.1007/s00158-003-0368-6.
- Meyer, R. K., and C. J. Nachtsheim. 1995. The coordinate-exchange algorithm for constructing exact optimal experimental designs. Technometrics 37 (1):60–9. doi:10.1080/00401706.1995.10485889.
- Myers, R. H., D. C. Montgomery, and C. M. Anderson-Cook. 2016. Response surface methodology: Process and product optimization using designed experiments. 4th ed. New York: Wiley.
- Reuter, H. 1990. An approximation method for the efficiency set of multiobjective programming problems. Optimization 21 (6):905–11. doi:10.1080/02331939008843621.
- Rodriguez, M., B. Jones, C. M. Borror, and D. C. Montgomery. 2010. Generating and assessing exact G-optimal designs. Journal of Quality Technology 42 (1):3–20. doi:10.1080/00224065.2010.11917803.
- Shi, Y., Z. Zhang, and W. Wong. 2019. Particle swarm-based algorithms for finding locally and Bayesian D-optimal designs. Journal of Statistical Distributions and Applications 6 (1):1–17. doi:10.1186/s40488-019-0092-4.
- Trautmann, H., and J. Mehnen. 2009. Preference-based Pareto optimization in certain and noisy environments. Engineering Optimization 41 (1):23–38. doi:10.1080/03052150802347926.
- Walsh, S. J. 2021. Development and applications of particle swarm optimization for constructing optimal experimental designs. Ph.D. Dissertation., Montana State University. https://scholarworks.montana.edu/xmlui/handle/1/16309.
- Walsh, S. J., and J. J. Borkowski. 2022. Generating exact optimal designs via particle swarm optimization: assessing efficacy and efficiency via case study. Quality Engineering 1–20. doi:10.1080/08982112.2022.2127364.
- Walsh, S. J., and J. J. Borkowski. 2022. Improved G-optimal designs for small exact response surface scenarios: fast and efficient generation via particle swarm optimization. Mathematics 10 (22):4245. doi:10.3390/math10224245.
- Wong, W. K., R. B. Chen, C. C. Huang, and W. Wang. 2015. 06 A modified particle swarm optimization technique for finding optimal designs for mixture models. PLoS ONE 10 (6):e0124720. doi:10.1371/journal.pone.0124720.
- Zahran, A., C. M. Anderson-Cook, and R. H. Myers. 2003. Fraction of design space to assess the prediction capability of response surface designs. Journal of Quality Technology 35 (4):377–86. doi:10.1080/00224065.2003.11980235.