Algoul, S., Alam, M. S., Hossain, M. A., & Majumder, M. A. A. (2010). Multi-objective optimal chemotherapy control model for cancer treatment. Medical & Biological Engineering & Computing, 49(1), 51-65. doi:10.1007/s11517-010-0678-y
Kiam Heong Ang, Chong, G., & Yun Li. (2005). PID control system analysis, design, and technology. IEEE Transactions on Control Systems Technology, 13(4), 559-576. doi:10.1109/tcst.2005.847331
Åström, K. J., & Hägglund, T. (2001). The future of PID control. Control Engineering Practice, 9(11), 1163-1175. doi:10.1016/s0967-0661(01)00062-4
[+]
Algoul, S., Alam, M. S., Hossain, M. A., & Majumder, M. A. A. (2010). Multi-objective optimal chemotherapy control model for cancer treatment. Medical & Biological Engineering & Computing, 49(1), 51-65. doi:10.1007/s11517-010-0678-y
Kiam Heong Ang, Chong, G., & Yun Li. (2005). PID control system analysis, design, and technology. IEEE Transactions on Control Systems Technology, 13(4), 559-576. doi:10.1109/tcst.2005.847331
Åström, K. J., & Hägglund, T. (2001). The future of PID control. Control Engineering Practice, 9(11), 1163-1175. doi:10.1016/s0967-0661(01)00062-4
Åström, K.J., Hägglund, T., 2005. Advanced PID Control. ISA - The Instrumentation, Systems, and Automation Society, Research Triangle Park, NC 27709.
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
Avigad, G., Moshaiov, A., Brauner, N., (2003). june Towards a general tool for mechatronic design. In: Control Applications, 2003. CCA 2003. Proceedings of 2003 IEEE Conference on. Vol. 2. pp. 1035-1040 vol.2.
Hultmann Ayala, H. V., & dos Santos Coelho, L. (2012). Tuning of PID controller based on a multiobjective genetic algorithm applied to a robotic manipulator. Expert Systems with Applications, 39(10), 8968-8974. doi:10.1016/j.eswa.2012.02.027
Behbahani, S., & de Silva, C. W. (2008). System-Based and Concurrent Design of a Smart Mechatronic System Using the Concept of Mechatronic Design Quotient (MDQ). IEEE/ASME Transactions on Mechatronics, 13(1), 14-21. doi:10.1109/tmech.2007.915058
Beyer, H.-G., & Sendhoff, B. (2007). Robust optimization – A comprehensive survey. Computer Methods in Applied Mechanics and Engineering, 196(33-34), 3190-3218. doi:10.1016/j.cma.2007.03.003
Bingül, Z., & Karahan, O. (2011). A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control. Expert Systems with Applications, 38(1), 1017-1031. doi:10.1016/j.eswa.2010.07.131
Biswas, A., Das, S., Abraham, A., & Dasgupta, S. (2009). Design of fractional-order PIλDμ controllers with an improved differential evolution. Engineering Applications of Artificial Intelligence, 22(2), 343-350. doi:10.1016/j.engappai.2008.06.003
Blasco, X., Herrero, J. M., Sanchis, J., & Martínez, M. (2008). A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization. Information Sciences, 178(20), 3908-3924. doi:10.1016/j.ins.2008.06.010
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
Caballero, J. A., & Grossmann, I. E. (2007). Una revisión del estado del arte en optimización. Revista Iberoamericana de Automática e Informática Industrial RIAI, 4(1), 5-23. doi:10.1016/s1697-7912(07)70188-7
Coello, C., 2000. Handling preferences in evolutionary multiobjective optimization: a survey. In: Evolutionary Computation, 2000. Proceedings of the 2000 Congress on. Vol. 1. pp. 30-37 vol.1.
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
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
Coello, C., 2011. An introduction to multi-objective particle swarm optimizers. In: Gaspar-Cunha, A., Takahashi, R., Schaefer, G., Costa, L. (Eds.), Soft Computing in Industrial Applications. Vol. 96 of Advances in Intelligent and Soft Computing. Springer Berlin /Heidelberg, pp. 3-12, 10,1007/978 − 3 − 642 − 20505 − 71.
Cordón, O. (2011). A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems. International Journal of Approximate Reasoning, 52(6), 894-913. doi:10.1016/j.ijar.2011.03.004
Corne, D.W., Knowles, J.D., 2007. Techniques for highly multiobjective optimisation: some nondominated points are better than others. In: Proceedings of the 9th annual conference on Genetic and evolutionary computation. GECCO ‘07. ACM, New York, NY, USA, pp. 773-780.
Cruz, C., González, J. R., & Pelta, D. A. (2010). Optimization in dynamic environments: a survey on problems, methods and measures. Soft Computing, 15(7), 1427-1448. doi:10.1007/s00500-010-0681-0
Das, S., Maity, S., Qu, B.-Y., & Suganthan, P. N. (2011). Real-parameter evolutionary multimodal optimization — A survey of the state-of-the-art. Swarm and Evolutionary Computation, 1(2), 71-88. doi:10.1016/j.swevo.2011.05.005
Das, R. T., Ang, K. K., & Quek, C. (2010). A synergy of econometrics and computational methods (GARCH-RNFS) for volatility forecasting. IEEE Congress on Evolutionary Computation. doi:10.1109/cec.2010.5586324
Das, S., Suganthan, P., 2011. Problem definitions and evaluation criteria for cec 2011 competition on testing evolutionary algorithms on real world optimization problems. Tech. rep., Jadavpur university and Nanyang Technological University.
Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2-4), 311-338. doi:10.1016/s0045-7825(99)00389-8
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. doi:10.1109/4235.996017
Dixon, R., & Pike, A. W. (2006). Alstom Benchmark Challenge II on Gasifier Control. IEE Proceedings - Control Theory and Applications, 153(3), 254-261. doi:10.1049/ip-cta:20050062
Eiben, A. E., & Schippers, C. A. (1998). On Evolutionary Exploration and Exploitation. Fundamenta Informaticae, 35(1-4), 35-50. doi:10.3233/fi-1998-35123403
Elgammal, A. A. A., & Sharaf, A. M. (2012). Self-regulating particle swarm optimised controller for (photovoltaic–fuel cell) battery charging of hybrid electric vehicles. IET Electrical Systems in Transportation, 2(2), 77. doi:10.1049/iet-est.2011.0021
Fazendeiro, P., de Oliveira, J. V., & Pedrycz, W. (2007). A Multiobjective Design of a Patient and Anaesthetist-Friendly Neuromuscular Blockade Controller. IEEE Transactions on Biomedical Engineering, 54(9), 1667-1678. doi:10.1109/tbme.2007.895109
Fazzolari, M., Alcala, R., Nojima, Y., Ishibuchi, H., & Herrera, F. (2013). A Review of the Application of Multiobjective Evolutionary Fuzzy Systems: Current Status and Further Directions. IEEE Transactions on Fuzzy Systems, 21(1), 45-65. doi:10.1109/tfuzz.2012.2201338
Figueira, J., Greco, S., Ehrgott, M., 2005. Multiple criteria decision analysis: State of the art surveys. Springer international series.
Fleming, P. ., & Purshouse, R. . (2002). Evolutionary algorithms in control systems engineering: a survey. Control Engineering Practice, 10(11), 1223-1241. doi:10.1016/s0967-0661(02)00081-3
Fonseca, C. M., & Fleming, P. J. (1998). Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 28(1), 26-37. doi:10.1109/3468.650319
Gaing, Z.-L. (2004). A Particle Swarm Optimization Approach for Optimum Design of PID Controller in AVR System. IEEE Transactions on Energy Conversion, 19(2), 384-391. doi:10.1109/tec.2003.821821
Goldberg, D., 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA.
Hajiloo, A., Nariman-zadeh, N., & Moeini, A. (2012). Pareto optimal robust design of fractional-order PID controllers for systems with probabilistic uncertainties. Mechatronics, 22(6), 788-801. doi:10.1016/j.mechatronics.2012.04.003
Herreros, A., Baeyens, E., & Perán, J. R. (2002). Design of PID-type controllers using multiobjective genetic algorithms. ISA Transactions, 41(4), 457-472. doi:10.1016/s0019-0578(07)60102-5
HUANG, L., WANG, N., & ZHAO, J.-H. (2008). Multiobjective Optimization for Controller Design. Acta Automatica Sinica, 34(4), 472-477. doi:10.3724/sp.j.1004.2008.00472
Inselberg, A. (1985). The plane with parallel coordinates. The Visual Computer, 1(2), 69-91. doi:10.1007/bf01898350
Iruthayarajan, M. W., & Baskar, S. (2009). Evolutionary algorithms based design of multivariable PID controller. Expert Systems with Applications, 36(5), 9159-9167. doi:10.1016/j.eswa.2008.12.033
Kamath, S., George, V. I., & Vidyasagar, S. (2009). A comparative study of different types of controllers used for blood glucose regulation system. The Canadian Journal of Chemical Engineering, 87(5), 812-817. doi:10.1002/cjce.20219
Kaveh, P., & Shtessel, Y. B. (2008). Blood glucose regulation using higher-order sliding mode control. International Journal of Robust and Nonlinear Control, 18(4-5), 557-569. doi:10.1002/rnc.1223
Kollat, J. B., & Reed, P. (2007). A framework for Visually Interactive Decision-making and Design using Evolutionary Multi-objective Optimization (VIDEO). Environmental Modelling & Software, 22(12), 1691-1704. doi:10.1016/j.envsoft.2007.02.001
Konak, A., Coit, D.W., Smith, A.E., 2006. Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety 91 (9), 992-1007, special Issue - Genetic Algorithms and Reliability.
Koza, J. R., Keane, M. A., & Streeter, M. J. (2003). What’s AI done for me lately? Genetic programming’s human-competitive results. IEEE Intelligent Systems, 18(3), 25-31. doi:10.1109/mis.2003.1200724
Koza, J., Poli, R., 2005. Genetic programming. In: Burke, E.K., Kendall, G. (Eds.), Search Methodologies. Springer US, pp. 127-164, 10.1007/0-387-28356-0 5.
Lamanna, R., Vega, P., Revollar, S., & Alvarez, H. (2009). Diseño Simultáneo de Proceso y Control de una Torre Sulfitadora de Jugo de Caña de Azúcar. Revista Iberoamericana de Automática e Informática Industrial RIAI, 6(3), 32-43. doi:10.1016/s1697-7912(09)70262-6
Lee, C.-H., & Chang, F.-K. (2010). Fractional-order PID controller optimization via improved electromagnetism-like algorithm. Expert Systems with Applications, 37(12), 8871-8878. doi:10.1016/j.eswa.2010.06.009
PID control system analysis and design. (2006). IEEE Control Systems, 26(1), 32-41. doi:10.1109/mcs.2006.1580152
Lin, C.-M., Li, M.-C., Ting, A.-B., & Lin, M.-H. (2011). A robust self-learning PID control system design for nonlinear systems using a particle swarm optimization algorithm. International Journal of Machine Learning and Cybernetics, 2(4), 225-234. doi:10.1007/s13042-011-0021-4
Lotov, A., Miettinen, K., 2008. Visualizing the Pareto frontier. In: Branke, J., Deb, K., Miettinen, K., Slowinski, R. (Eds.), Multiobjective Optimization. Vol. 5252 of Lecture Notes in Computer Science. Springer Berlin /Heidelberg, pp. 213-243.
Luyben, W. L. (1986). Simple method for tuning SISO controllers in multivariable systems. Industrial & Engineering Chemistry Process Design and Development, 25(3), 654-660. doi:10.1021/i200034a010
Mallipeddi, R., Suganthan, P., 2009. Problem definitions and evaluation criteria for the CEC 2010 competition on constrained real-parameter optimization. Tech. rep., Nanyang Technological University, Singapore.
Marler, R. T., & Arora, J. S. (2004). Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization, 26(6), 369-395. doi:10.1007/s00158-003-0368-6
Mattson, C.A., Messac, A., 2005 Pareto frontier based concept selection under uncertainty, with visualization. Optimization and Engineering 6, 85-115, 10.1023/B:OPTE. 0000048538.35456.45.
Menhas, M. I., Fei, M., Wang, L., & Qian, L. (2012). Real/binary co-operative and co-evolving swarms based multivariable PID controller design of ball mill pulverizing system. Energy Conversion and Management, 54(1), 67-80. doi:10.1016/j.enconman.2011.10.001
Menhas, M. I., Wang, L., Fei, M., & Pan, H. (2012). Comparative performance analysis of various binary coded PSO algorithms in multivariable PID controller design. Expert Systems with Applications, 39(4), 4390-4401. doi:10.1016/j.eswa.2011.09.152
Messac, A. (1996). Physical programming - Effective optimization for computational design. AIAA Journal, 34(1), 149-158. doi:10.2514/3.13035
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
Mezura-Montes, E., Reyes-Sierra, M., & Coello, C. A. C. (2008). Multi-objective Optimization Using Differential Evolution: A Survey of the State-of-the-Art. Studies in Computational Intelligence, 173-196. doi:10.1007/978-3-540-68830-3_7
Oh, S.-K., Kim, W.-D., & Pedrycz, W. (2012). Design of optimized cascade fuzzy controller based on differential evolution: Simulation studies and practical insights. Engineering Applications of Artificial Intelligence, 25(3), 520-532. doi:10.1016/j.engappai.2012.01.002
Pan, I., Das, S., & Gupta, A. (2011). Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay. ISA Transactions, 50(1), 28-36. doi:10.1016/j.isatra.2010.10.005
Rao, J. S., & Tiwari, R. (2009). Design optimization of double-acting hybrid magnetic thrust bearings with control integration using multi-objective evolutionary algorithms. Mechatronics, 19(6), 945-964. doi:10.1016/j.mechatronics.2009.06.011
Reynoso-Meza, G., Blasco, X., & Sanchis, J. (2009). Diseño Multiobjetivo de controladores PID para el Benchmark de Control 2008–2009. Revista Iberoamericana de Automática e Informática Industrial RIAI, 6(4), 93-103. doi:10.1016/s1697-7912(09)70112-8
Reynoso-Meza, G., Sanchis, J., Blasco, X., Herrero, J., september 2011a. Handling control engineer preferences: Getting the most of PI controllers. In: Emerging Technologies Factory Automation (ETFA), 2011 IEEE 16th Conference on. pp. 1-8.
Reynoso-Meza, G., Sanchis, J., Blasco, X., Herrero, J., june 2011b. Hybrid DE algorithm with adaptive crossover operator for solving real-world numerical optimization problems. In: Evolutionary Computation (CEC), 2011 IEEE Congress on. pp. 1551-1556.
Reynoso-Meza, G., Blasco, X., Sanchis, J., March 2012a. Optimización evolutiva multi-objetivo y selección multi-criterio para la ingeniería de control. In: X Simposio CEA de Ingeniería de Control.
Reynoso-Meza, G., García-Nieto, S., Sanchis, J., Blasco, X., 2012b. Controller tuning using multiobjective optimization algorithms: a global tuning framework. IEEE Transactions on Control Systems Article in press.
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
Reynoso-Meza, G., Blasco, X., Sanchis, J., & Herrero, J. M. (2013). Comparison of design concepts in multi-criteria decision-making using level diagrams. Information Sciences, 221, 124-141. doi:10.1016/j.ins.2012.09.049
Romero-Pérez, J. A., Arrieta, O., Padula, F., Reynoso-Meza, G., Garcia-Nieto, S., & Balaguer, P. (2012). Estudio comparativo de algoritmos de auto-ajuste de controladores PID. Resultados del Benchmark 2010-2011 del Grupo de Ingeniería de Control de CEA. Revista Iberoamericana de Automática e Informática Industrial RIAI, 9(2), 182-193. doi:10.1016/j.riai.2012.02.009
Roy, R., Hinduja, S., & Teti, R. (2008). Recent advances in engineering design optimisation: Challenges and future trends. CIRP Annals, 57(2), 697-715. doi:10.1016/j.cirp.2008.09.007
Sanchis, J., Martínez, M. A., Blasco, X., & Reynoso-Meza, G. (2010). Modelling preferences in multi-objective engineering design. Engineering Applications of Artificial Intelligence, 23(8), 1255-1264. doi:10.1016/j.engappai.2010.07.005
Santana-Quintero, L. V., Montaño, A. A., & Coello, C. A. C. (2010). A Review of Techniques for Handling Expensive Functions in Evolutionary Multi-Objective Optimization. Evolutionary Learning and Optimization, 29-59. doi:10.1007/978-3-642-10701-6_2
Saridakis, K. M., & Dentsoras, A. J. (2008). Soft computing in engineering design – A review. Advanced Engineering Informatics, 22(2), 202-221. doi:10.1016/j.aei.2007.10.001
Shi, L., & Rasheed, K. (2010). A Survey of Fitness Approximation Methods Applied in Evolutionary Algorithms. Evolutionary Learning and Optimization, 3-28. doi:10.1007/978-3-642-10701-6_1
Panda, S. (2011). Multi-objective PID controller tuning for a FACTS-based damping stabilizer using Non-dominated Sorting Genetic Algorithm-II. International Journal of Electrical Power & Energy Systems, 33(7), 1296-1308. doi:10.1016/j.ijepes.2011.06.002
Skogestad, S. (2003). Simple analytic rules for model reduction and PID controller tuning. Journal of Process Control, 13(4), 291-309. doi:10.1016/s0959-1524(02)00062-8
Storn, R., & Price, K. (1997). Journal of Global Optimization, 11(4), 341-359. doi:10.1023/a:1008202821328
Tan, W., Liu, J., Fang, F., & Chen, Y. (2004). Tuning of PID controllers for boiler-turbine units. ISA Transactions, 43(4), 571-583. doi:10.1016/s0019-0578(07)60169-4
Tan, W. W., Lu, F., Loh, A. P., & Tan, K. C. (2005). Modeling and control of a pilot pH plant using genetic algorithm. Engineering Applications of Artificial Intelligence, 18(4), 485-494. doi:10.1016/j.engappai.2004.11.006
Vilanova, R., & Alfaro, V. M. (2011). Control PID robusto: Una visión panorámica. Revista Iberoamericana de Automática e Informática Industrial RIAI, 8(3), 141-158. doi:10.1016/j.riai.2011.06.003
Xue, Y., Li, D., & Gao, F. (2010). Multi-objective optimization and selection for the PI control of ALSTOM gasifier problem. Control Engineering Practice, 18(1), 67-76. doi:10.1016/j.conengprac.2009.09.004
Zamani, M., Karimi-Ghartemani, M., Sadati, N., & Parniani, M. (2009). Design of a fractional order PID controller for an AVR using particle swarm optimization. Control Engineering Practice, 17(12), 1380-1387. doi:10.1016/j.conengprac.2009.07.005
Zhang, J., Zhuang, J., Du, H., & Wang, S. (2009). Self-organizing genetic algorithm based tuning of PID controllers. Information Sciences, 179(7), 1007-1018. doi:10.1016/j.ins.2008.11.038
Zhao, S.-Z., Iruthayarajan, M. W., Baskar, S., & Suganthan, P. N. (2011). Multi-objective robust PID controller tuning using two lbests multi-objective particle swarm optimization. Information Sciences, 181(16), 3323-3335. doi:10.1016/j.ins.2011.04.003
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
[-]
