Marler, R. T., & Arora, J. S. (2009). The weighted sum method for multi-objective optimization: new insights. Structural and Multidisciplinary Optimization, 41(6), 853-862. doi:10.1007/s00158-009-0460-7
Messac, A., & Mattson, C. A. (2002). Optimization and Engineering, 3(4), 431-450. doi:10.1023/a:1021179727569
Messac, A., Ismail-Yahaya, A., & Mattson, C. A. (2003). The normalized normal constraint method for generating the Pareto frontier. Structural and Multidisciplinary Optimization, 25(2), 86-98. doi:10.1007/s00158-002-0276-1
[+]
Marler, R. T., & Arora, J. S. (2009). The weighted sum method for multi-objective optimization: new insights. Structural and Multidisciplinary Optimization, 41(6), 853-862. doi:10.1007/s00158-009-0460-7
Messac, A., & Mattson, C. A. (2002). Optimization and Engineering, 3(4), 431-450. doi:10.1023/a:1021179727569
Messac, A., Ismail-Yahaya, A., & Mattson, C. A. (2003). The normalized normal constraint method for generating the Pareto frontier. Structural and Multidisciplinary Optimization, 25(2), 86-98. doi:10.1007/s00158-002-0276-1
Martínez, M., Sanchis, J., & Blasco, X. (2006). Global and well-distributed Pareto frontier by modified normalized normal constraint methods for bicriterion problems. Structural and Multidisciplinary Optimization, 34(3), 197-209. doi:10.1007/s00158-006-0071-5
Martínez, M., García-Nieto, S., Sanchis, J., & Blasco, X. (2009). Genetic algorithms optimization for normalized normal constraint method under Pareto construction. Advances in Engineering Software, 40(4), 260-267. doi:10.1016/j.advengsoft.2008.04.004
Mattson, C. A., & Messac, A. (2005). Pareto Frontier Based Concept Selection Under Uncertainty, with Visualization. Optimization and Engineering, 6(1), 85-115. doi:10.1023/b:opte.0000048538.35456.45
Mattson, C. A., Mullur, A. A., & Messac, A. (2004). Smart Pareto filter: obtaining a minimal representation of multiobjective design space. Engineering Optimization, 36(6), 721-740. doi:10.1080/0305215042000274942
Coello Coello, C. A. (2006). Evolutionary multi-objective optimization: a historical view of the field. IEEE Computational Intelligence Magazine, 1(1), 28-36. doi:10.1109/mci.2006.1597059
Zhou, A., Qu, B.-Y., Li, H., Zhao, S.-Z., Suganthan, P. N., & Zhang, Q. (2011). Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation, 1(1), 32-49. doi:10.1016/j.swevo.2011.03.001
Bonissone, P., Subbu, R., & Lizzi, J. (2009). Multicriteria decision making (mcdm): a framework for research and applications. IEEE Computational Intelligence Magazine, 4(3), 48-61. doi:10.1109/mci.2009.933093
Laumanns, M., Thiele, L., Deb, K., & Zitzler, E. (2002). Combining Convergence and Diversity in Evolutionary Multiobjective Optimization. Evolutionary Computation, 10(3), 263-282. doi:10.1162/106365602760234108
Coello Coello, C. A. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering, 191(11-12), 1245-1287. doi:10.1016/s0045-7825(01)00323-1
Mezura-Montes, E., & Coello Coello, C. A. (2011). Constraint-handling in nature-inspired numerical optimization: Past, present and future. Swarm and Evolutionary Computation, 1(4), 173-194. doi:10.1016/j.swevo.2011.10.001
ÅSTRÖM, K. J., PANAGOPOULOS, H., & HÄGGLUND, T. (1998). Design of PI Controllers based on Non-Convex Optimization. Automatica, 34(5), 585-601. doi:10.1016/s0005-1098(98)00011-9
Reynoso-Meza, G., Sanchis, J., Blasco, X., & Herrero, J. M. (2012). Multiobjective evolutionary algorithms for multivariable PI controller design. Expert Systems with Applications, 39(9), 7895-7907. doi:10.1016/j.eswa.2012.01.111
[-]