Výsledky vyhledávání - modified multi-objective (particle OR articles) swart optimization algorithm
-
1
Autoři: a další
Zdroj: Applied Sciences (2076-3417); Apr2025, Vol. 15 Issue 7, p4005, 28p
-
2
Autoři:
Zdroj: ACM Transactions on the Web; May2025, Vol. 19 Issue 2, p1-34, 34p
-
3
Autoři: a další
Zdroj: Materials (1996-1944); Feb2023, Vol. 16 Issue 3, p1050, 11p
-
4
Autoři: a další
Zdroj: Tecnura; Vol. 26 No. 74 (2022): October - December ; 87-129 ; Tecnura; Vol. 26 Núm. 74 (2022): Octubre - Diciembre ; 2248-7638 ; 0123-921X
Témata: Optimal power flow problem, metaheuristic optimization, second-order cone programming, convex optimization, distributed generation, branch power flow, flujo de potencia óptimo, optimización metaheurística, programación cónica de segundo orden, optimización convexa, generación distribuida, flujo de potencia
Popis souboru: application/pdf; text/xml
Relation: https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/18342/18512; https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/18342/18586; Abido, M. A. (2002). Optimal power flow using particle swarm optimization. International Journal of Electrical Power & Energy Systems, 24(7), 563-571. https://doi.org/10.1016/S0142-0615(01)00067-9; Abou El Ela, A. A., Abido, M. A., & Spea, S. R. (2010). Optimal power flow using differential evolution algorithm. Electric Power Systems Research, 80(7), 878-885. https://doi.org/10.1016/j.epsr.2009.12.018; Abo-Elnaga, Y., & El-Shorbagy, M. A. (2020). Multi-sine cosine algorithm for solving nonlinear bilevel programming problems. International Journal of Computational Intelligence Systems, 13(1), 421-432. https://doi.org/10.2991/ijcis.d.200411.001; Akbar, N. S., & Nadeem, S. (2014). Carreau fluid model for blood flow through a tapered artery with a stenosis. Ain Shams Engineering Journal, 5(4), 1307-1316. https://doi.org/10.1016/j.asej.2014.05.010; Andersen, M. S., Hansson, A., & Vandenberghe, L. (2013). Reduced-complexity semidefinite relaxations of optimal power flow problems. IEEE Transactions on Power Systems, 29(4), 1855-1863. https://doi.org/10.1109/TPWRS.2013.2294479; Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Computers & structures, 169, 1-12. https://doi.org/10.1016/j.compstruc.2016.03.001; Attia, A. F., El Sehiemy, R. A., & Hasanien, H. M. (2018). Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. International Journal of Electrical Power & Energy Systems, 99, 331-343. https://doi.org/10.1016/j.ijepes.2018.01.024; Bai, X., Wei, H., Fujisawa, K., & Wang, Y. (2008). Semidefinite programming for optimal power flow problems. International Journal of Electrical Power & Energy Systems, 30(6-7), 383-392. https://doi.org/10.1016/j.ijepes.2007.12.003; Baradar, M., Hesamzadeh, M. R., & Ghandhari, M. (2013). Second-order cone programming for optimal power flow in VSC-type AC-DC grids. IEEE Transactions on Power Systems, 28(4), 4282-4291. https://doi.org/10.1109/TPWRS.2013.2271871; Bayat, A., & Bagheri, A. (2019). Optimal active and reactive power allocation in distribution networks using a novel heuristic approach. Applied Energy, 233, 71-85. https://doi.org/10.1016/j.apenergy.2018.10.030; Ben Oualid Medani, K., Sayah, S., & Bekrar, A. (2018). Whale optimization algorithm based optimal reactive power 620 dispatch: A case study of the Algerian power system. Electrical Power Systems Research, 163, 696-705. https://doi.org/10.1016/j.epsr.2017.09.001; Benson, H. Y., & Sağlam, Ü. (2013). Mixed-integer second-order cone programming: A survey. Theory Driven by Influential Applications, 2013, 13-36. https://doi.org/10.1287/educ.2013.0115; Bocanegra, S. Y., & Montoya, O. D. (2019). Heuristic approach for optimal location and sizing of distributed generators in AC distribution networks. https://hdl.handle.net/20.500.12585/9176; Boyd, S. P., & Vandenberghe, L. (2004). Convex optimization. Cambridge University Press. https://doi.org/10.1017/CBO9780511804441; Bouchekara, H. R. E. H. (2013). Optimal design of electromagnetic devices using a black-hole-based optimization technique. IEEE Transactions on Magnetics, 49(12), 5709-5714. https://doi.org/10.1109/TMAG.2013.2277694; Bouchekara, H. R. E. H., Abido, M. A., & Boucherma, M. (2014). Optimal power flow using teaching-learning-based optimization technique. Electric Power Systems Research, 114, 49-59. https://doi.org/10.1016/j.epsr.2014.03.032; Cawley, J., & Ruhm, C. J. (2011). The economics of risky health behaviors. In M. V. Pauly, T. G. Mcguire, & P. P. Barros (Eds.), Handbook of Health Economics (vol. 2, pp. 95-199). Elsevier. https://doi.org/10.3386/w17081; Chen, G., Yi, X., Zhang, Z., & Lei, H. (2018). Solving the multi-objective optimal power flow problem using the multi-objective firefly algorithm with a constraints-prior Pareto-domination approach. Energies, 11(12), 3438. https://doi.org/10.3390/en11123438; Davoodi, E., Babaei, E., Mohammadi-Ivatloo, B., Shafie-Khah, M., & Catalão, J. P. (2020). Multiobjective optimal power flow using a semidefinite programming-based model. IEEE Systems Journal, 15(1), 158-169. https://doi.org/10.1109/JSYST.2020.2971838; Deshmukh, R., & Kalage, A. (2018, November 23-24). Optimal placement and sizing of distributed generator in distribution system using artificial bee colony algorithm [Conference presentation]. 2018 IEEE Global Conference on Wireless Computing and Networking, Lonavala, India. https://doi.org/10.1109/GCWCN.2018.8668633 Devabalaji, K. R., Imran, A. M., Yuvaraj, T., & Ravi, K. J. E. P. (2015). Power loss minimization in radial distribution system. Energy Procedia, 79, 917-923. https://doi.org/10.1016/j.egypro.2015.11.587; Doğan, B., & Ölmez, T. (2015). A new metaheuristic for numerical function optimization: Vortex Search algorithm. Information Sciences, 293, 125-145. https://doi.org/10.1016/j.ins.2014.08.053; El-Fergany, A. A., & Hasanien, H. M. (2015). Single and multi-objective optimal power flow using grey wolf optimizer and differential evolution algorithms. Electric Power Components and Systems, 43(13), 1548-1559. https://doi.org/10.1080/15325008.2015.1041625; Ebeed, M., Kamel, S., & Jurado, F. (2018). Optimal power flow using recent optimization techniques. In Classical and recent aspects of power system optimization (pp. 157-183). Academic Press. https://doi.org/10.1016/B978-0-12-812441-3.00007-0; El-Khattam, W., & Salama, M. M. (2004). Distributed generation technologies, definitions, and benefits.E lectric Power Systems Research, 71(2), 119-128. https://doi.org/10.1016/j.epsr.2004.01.006; Farivar, M., & Low, S. H. (2013). Branch flow model: Relaxations and convexification – Part I. IEEE Transactions on Power Systems, 28(3), 2554-2564. https://doi.org/10.1109/TPWRS.2013.2255317; Garcés, A. (2016). A quadratic approximation for the optimal power flow in power distribution systems. Electric Power Systems Research, 130, 222-229. https://doi.org/10.1016/j.epsr.2015.09.006; Gharehchopogh, F. S., Maleki, I., & Dizaji, Z. A. (2021). Chaotic vortex search algorithm: Metaheuristic algorithm for feature selection. Evolutionary Intelligence, 15, 1777-1808. https://doi.org/10.1007/s12065-021-00590-1; Gholami, K., & Parvaneh, M. H. (2019). A mutated salp swarm algorithm for optimum allocation of active and reactive power sources in radial distribution systems. Applied Soft Computing, 85, 105833. https://doi.org/10.1016/j.asoc.2019.105833; Gil-González, W., Montoya, O. D., Rajagopalan, A., Grisales-Noreña, L. F., & Hernández, J. C. (2020). Optimal selection and location of fixed-step capacitor banks in distribution networks using a discrete version of the vortex search algorithm. Energies, 13(18), 4914. https://doi.org/10.3390/en13184914; Grisales-Noreña, L. F., González-Montoya, D., & Ramos-Paja, C. A. (2018). Optimal sizing and location of distributed generators based on PBIL and PSO techniques. Energies, 11(4), 1018. https://doi.org/10.3390/en11041018; Grisales-Noreña, L. F., Garzón-Rivera, O. D., Ocampo-Toro, J. A., Ramos-Paja, C. A., & Rodríguez-Cabal, M. A. (2020). Metaheuristic optimization methods for optimal power flow analysis in DC distribution networks. Transactions on Energy Systems and Engineering Applications, 1(1), 13-31. https://doi.org/10.32397/tesea.vol1.n1.2; Gupta, S., Saxena, A., & Soni, B. P. (2015). Optimal placement strategy of distributed generators based on radial basis function neural network in distribution networks. Procedia Computer Science, 57, 249-257. https://doi.org/10.1016/j.procs.2015.07.478; Hasan, Z., & El-Hawary, M. E. (2014, November 12-14). Optimal power flow by black hole optimization algorithm [Conference presentation]. 2014 IEEE Electrical Power and Energy Conference, Calgary, AB, Canada. https://doi.org/10.1109/EPEC.2014.43; Gutiérrez, D., Villa, W. M., & López-Lezama, J. M. (2017). Flujo óptimo reactivo mediante optimización por enjambre de partículas. Información Tecnológica, 28(5), 215-224. https://doi.org/10.4067/S0718-07642017000500020; Hariharan, T., & Sundaram, K. M. (2016). Optimal power flow using firefly algorithm with unified power flow controller. Circuits and Systems, 7(08), 1934. https://doi.org/10.4236/cs.2016.78168; Herbadji, O., Nadhir, K., Slimani, L., & Bouktir, T. (2013, April 28-30). Optimal power flow with emission controlled using firefly algorithm [Conference presentation]. 2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), Hammamet, Tunisia. https://doi.org/10.1109/ICMSAO.2013.6552559; Hernández, C., Sánchez-Huertas, W., & Gómez, V. (2021). Optimal power flow through artificial intelligence techniques. Tecnura, 25(69), 150-170. https://doi.org/10.14483/22487638.18245; Home-Ortiz, J. M., Pourakbari-Kasmaei, M., Lehtonen, M., & Mantovani, J. R. S. (2019). Optimal location-allocation of storage devices and renewable-based DG in distribution systems. Electric Power Systems Research, 172, 11-21. https://doi.org/10.1016/j.epsr.2019.02.013; Hudaib, A. A., & Fakhouri, H. N. (2018). Supernova optimizer: A novel natural inspired meta-heuristic. Modern Applied Science, 12(1), 32-50. https://doi.org/10.5539/mas.v12n1p32; Injeti, S. K., & Kumar, N. P. (2013). A novel approach to identify optimal access point and capacity of multiple DGs in a small, medium and large scale radial distribution systems. International Journal of Electrical Power & Energy Systems, 45(1), 142-151. https://doi.org/10.1016/j.ijepes.2012.08.043; Kaur, S., Kumbhar, G., & Sharma, J. (2014). A MINLP technique for optimal placement of multiple DG units in distribution systems. International Journal of Electrical Power & Energy Systems, 63, 609-617. https://doi.org/10.1016/j.ijepes.2014.06.023; Khan, B., & Singh, P. (2017). Optimal power flow techniques under characterization of conventional and renewable energy sources: A comprehensive analysis. Journal of Engineering, 2017, 9539506. https://doi.org/10.1155/2017/9539506; Khan, A., Hizam, H., Abdul-Wahab, N. I., & Othman, M. L. (2020). Solution of optimal power flow using non-dominated sorting multi objective based hybrid firefly and particle swarm optimization algorithm. Energies, 13(16), 4265. https://doi.org/10.3390/en13164265; Kronqvist, J., Bernal, D. E., Lundell, A., & Grossmann, I. E. (2019). A review and comparison of solvers for convex MINLP. Optimization and Engineering, 20(2), 397-455. https://doi.org/10.1007/s11081-018-9411-8; Mitchell, M. (1998). An introduction to genetic algorithms. MIT press. https://doi.org/9780262133166; Lakshmi, P., Rao, B. V., Devarapalli, R., & Rai, P. (2020, July 10-11). Optimal power flow with BAT algorithm for a power system to reduce transmission line losses using SVC [Conference presentation]. 2020 International Conference on Emerging Frontiers in Electrical and Electronic Technologies, Patna, India. https://doi.org/10.1109/ICEFEET49149.2020.9186964; Lavaei, J., & Low, S. H. (2011). Zero duality gap in optimal power flow problem. IEEE Transactions on Power Systems, 27(1), 92-107. https://doi.org/10.1109/TPWRS.2011.2160974; Lavorato, M., Franco, J. F., Rider, M. J., & Romero, R. (2011). Imposing radiality constraints in distribution system optimization problems. IEEE Transactions on Power Systems, 27(1), 172-180. https://doi.org/10.1109/TPWRS.2011.2161349; Lima, J. Q., & Barán, B. (2006). Optimización de enjambre de partículas aplicada al problema del cajero viajante bi-objetivo. Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial, 10(32), 67-76.; Manrique, M. L., Montoya, O. D., Garrido, V. M., Grisales-Noreña, L. F., & Gil-González, W. (2019). Sine-cosine algorithm for OPF analysis in distribution systems to size distributed generators. In J. C. Figueroa-García, M. Duarte-González, S. Jaramillo-Isaza, Á. D. Orjuela-Cañón, & Y. Díaz-Gutierrez (Eds.), WEA 2019: Applied Computer Sciences in Engineering (pp. 28-39). Springer. https://doi.org/10.1007/978-3-030-31019-6_3; Marini, A., Mortazavi, S. S., Piegari, L., & Ghazizadeh, M. S. (2019). An efficient graph-based power flow algorithm for electrical distribution systems with a comprehensive modeling of distributed generations. Electric Power Systems Research, 170, 229-243. https://doi.org/10.1016/j.epsr.2018.12.026; Mirjalili, S. M., Mirjalili, S. Z., Saremi, S., & Mirjalili, S. (2020). Sine cosine algorithm: theory, literature review, and application in designing bend photonic crystal waveguides. In S. Mirijalili, J. Song Dong, & A. Lewis (Eds.), Nature-Inspired Optimizers. Studies in Computational Intelligence (vol. 811, pp. 201-217). Springer. https://doi.org/10.1007/978-3-030-12127-3_12; Mohagheghi, E., Alramlawi, M., Gabash, A., & Li, P. (2018). A survey of real-time optimal power flow. Energies, 11(11), 3142. https://doi.org/10.3390/en11113142; Molzahn, D. K., & Hiskens, I. A. (2016). Convex relaxations of optimal power flow problems: An illustrative example. IEEE Transactions on Circuits and Systems I: Regular Papers, 63(5), 650-660. https://doi.org/10.1109/TCSI.2016.2529281; Montoya-Giraldo, O. D., Gil-González, W. J., & Garcés-Ruíz, A. (2017). Flujo de potencia óptimo para redes radiales y enmalladas empleando programación semidefinida. TecnoLógicas, 20(40), 29-42. https://doi.org/10.22430/22565337.703; Montoya, O. D., Grisales-Noreña, L. F., Amin, W. T., Rojas, L. A., & Campillo, J. (2019). Vortex search algorithm for optimal sizing of distributed generators in AC distribution networks with radial topology. In J. C. Figueroa-García, M. Duarte-González, S. Jaramillo-Isaza, Á. D. Orjuela-Cañón, & Y. Díaz-Gutierrez (Eds.), WEA 2019: Applied Computer Sciences in Engineering (pp. 235-249). Springer. https://doi.org/10.1007/978-3-030-31019-6_21; Montoya, O. D., Gil-González, W., & Giral, D. A. (2020a). On the matricial formulation of iterative sweep power flow for radial and meshed distribution networks with guarantee of convergence. Applied Sciences, 10(17), 5802. https://doi.org/10.3390/app10175802; Montoya, O. D., Gil-González, W., & Orozco-Henao, C. (2020b). Vortex search and Chu-Beasley genetic algorithms for optimal location and sizing of distributed generators in distribution networks: A novel hybrid approach. Engineering Science and Technology, an International Journal, 23(6), 1351-1363. https://doi.org/10.1016/j.jestch.2020.08.002; Montoya, O. D., Gil-González, W., Serra, F. M., Hernández, J. C., & Molina-Cabrera, A. (2020c). A second-order cone programming reformulation of the economic dispatch problem of BESS for apparent power compensation in ac distribution networks. Electronics, 9(10), 1677. https://doi.org/10.3390/electronics9101677; Montoya, O. D., & Gil-González, W. (2020). On the numerical analysis based on successive approximations for power flow problems in AC distribution systems. Electric Power Systems Research, 187, 106454. https://doi.org/10.1016/j.epsr.2020.106454; Montoya, O. D., Arias-Londoño, A., & Molina-Cabrera, A. (2022). Branch optimal power flow model for DC networks with radial structure: A conic relaxation. Tecnura, 26(71), 30-42. https://doi.org/10.14483/22487638.18635; Moradi, M. H., & Abedini, M. (2012). A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. International Journal of Electrical Power & Energy Systems, 34(1), 66-74. https://doi.org/10.1016/j.ijepes.2011.08.023; Moradi, M. H., & Abedini, M. (2016). A novel method for optimal DG units’ capacity and location in Microgrids. International Journal of Electrical Power & Energy Systems, 75, 236-244. https://doi.org/10.1016/j.ijepes.2015.09.013; Mouassa, S., Bouktir, T., & Salhi, A. (2017). Ant lion optimizer for solving optimal reactive power dispatch problem in power systems. Engineering Science and Technology, an International Journal, 20(3), 885-895. https://doi.org/10.1016/j.jestch.2017.03.006; Mukherjee, A., & Mukherjee, V. (2015). Solution of optimal power flow using chaotic krill herd algorithm. Chaos, Solitons & Fractals, 78, 10-21. https://doi.org/10.1016/j.chaos.2015.06.020; Muthukumar, K., & Jayalalitha, S. (2016). Optimal placement and sizing of distributed generators and shunt capacitors for power loss minimization in radial distribution networks using hybrid heuristic search optimization technique. International Journal of Electrical Power & Energy Systems, 78, 299-319. https://doi.org/10.1016/j.ijepes.2015.11.019; Nowdeh, S. A., Davoudkhani, I. F., Moghaddam, M. H., Najmi, E. S., Abdelaziz, A. Y., Ahmadi, A., & Gandoman, F. H. (2019). Fuzzy multi-objective placement of renewable energy sources in distribution system with objective of loss reduction and reliability improvement using a novel hybrid method. Applied Soft Computing, 77, 761-779. https://doi.org/10.1016/j.asoc.2019.02.003; Ou, T. C. (2012). A novel unsymmetrical faults analysis for microgrid distribution systems. International Journal of Electrical Power & Energy Systems, 43(1), 1017-1024. https://doi.org/10.1016/j.ijepes.2012.05.012; Prior, H., Schwarz, A., & Güntürkün, O. (2008). Mirror-induced behavior in the magpie (Pica pica): Evidence of self-recognition. PLoS biology, 6(8), e202. https://doi.org/10.1371/journal.pbio.0060202; Radziukynas, V., & Radziukyniene, I. (2009). Optimization methods application to optimal power flow in electric power systems. In J. Kallrath, P. M. Pardalos, S. Rebennack & M. Scheidt (Eds.) Optimization in the Energy Industry. Energy Systems (pp. 409-436). Springer. https://doi.org/10.1007/978-3-540-88965-6_18; Raviprabhakaran, V., & Ravichandran, C. S. (2016). Enriched biogeography-based optimization algorithm to solve economic power dispatch problem. In M. Pant, K. Deep, J. Bansal, A. Nagar & K. Das (Eds.), Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing (vol. 437, pp. 875-888). Springer. https://doi.org/10.1007/978-981-10-0451-3_78; Raviprabakaran, V., & Subramanian, R. C. (2018). Enhanced ant colony optimization to solve the optimal power flow with ecological emission. International Journal of System Assurance Engineering and Management, 9(1), 58-65. https://doi.org/10.1007/s13198-016-0471-x; Rbouh, I., & El Imrani, A. A. (2014). Hurricane-based optimization algorithm. AASRI Procedia, 6, 26-33. https://doi.org/10.1016/j.aasri.2014.05.005; Rosli, S. J., Rahim, H. A., Abdul Rani, K. N., Ngadiran, R., Ahmad, R. B., Yahaya, N. Z., & Andrew, A. M. (2020). A hybrid modified method of the sine cosine algorithm using latin hypercube sampling with the cuckoo search algorithm for optimization problems. Electronics, 9(11), 1786. https://doi.org/10.3390/electronics9111786; Rupolo, D., Mantovani, J. R. S., & Junior, B. R. P. (2019, June 23-27). Medium- and low-voltage planning of electric power distribution systems with distributed generation, energy storage sources, and electric vehicles [Conference presentation]. 2019 IEEE Milan PowerTech Milan, Italy. https://doi.org/10.1109/PTC.2019.8810573; Rupolo, D., Pereira Junior, B. R., Contreras, J., & Mantovani, J. R. S. (2020). Multiobjective approach for medium- and low-voltage planning of power distribution systems considering renewable energy and robustness. Energies, 13(10), 2517. https://doi.org/10.1109/PTC.2019.8810573; Salkuti, S. R. (2019). Optimal power flow using multi-objective glowworm swarm optimization algorithm in a wind energy integrated power system. International Journal of Green Energy, 16(15), 1547-1561. https://doi.org/10.1080/15435075.2019.1677234; Scoble, M. J. (2005). Book review. Systematic Entomology, 30(3), 497-498. https://doi.org/10.1111/j.1365-3113.2005.00311.x; Shen, T., Li, Y., & Xiang, J. (2018). A graph-based power flow method for balanced distribution systems. Energies, 11(3), 511. https://doi.org/10.3390/en11030511; Siavash, M., Pfeifer, C., Rahiminejad, A., & Vahidi, B. (2017, May 17-19). An application of grey wolf optimizer for optimal power flow of wind integrated power systems [Conference presentation]. 2017 18th International Scientific Conference on Electric Power Engineering, Kouty nad Desnou, Czech Republic. https://doi.org/10.1109/EPE.2017.7967230; Simiyu, P., Xin, A., Wang, K., Adwek, G., & Salman, S. (2020). Multiterminal medium voltage DC distribution network hierarchical control. Electronics, 9(3), 506. https://doi.org/10.3390/electronics9030506; Sultana, S., & Roy, P. K. (2014). Multi-objective quasi-oppositional teaching learning-based optimization for optimal location of distributed generator in radial distribution systems. International Journal of Electrical Power & Energy Systems, 63, 534-545. https://doi.org/10.1016/j.ijepes.2014.06.031; Sultana, S., & Roy, P. K. (2016). Krill herd algorithm for optimal location of distributed generator in radial distribution system. Applied Soft Computing, 40, 391-404. https://doi.org/10.1016/j.asoc.2015.11.036; Taher, M. A., Kamel, S., Jurado, F., & Ebeed, M. (2019). Modified grasshopper optimization framework for optimal power flow solution. Electrical Engineering, 101(1), 121-148. https://doi.org/10.1007/s00202-019-00762-4; Tamilselvan, V., Jayabarathi, T., Raghunathan, T., & Yang, X. S. (2018). Optimal capacitor placement in radial distribution systems using flower pollination algorithm. Alexandria Engineering Journal, 57(4), 2775-2786. https://doi.org/10.1016/j.aej.2018.01.004; Tang, Y., Dvijotham, K., & Low, S. (2017). Real-time optimal power flow. IEEE Transactions on Smart Grid, 8(6), 2963-2973. https://doi.org/10.1109/TSG.2017.2704922; Topaloglu, H., Smith, J. C., & Greenberg, H. J. (Eds.) (2013). Theory driven by influential applications. Informs. https://doi.org/10.1287/educ.2013; Trivedi, I. N., Jangir, P., & Parmar, S. A. (2016). Optimal power flow with enhancement of voltage stability and reduction of power loss using ant-lion optimizer. Cogent Engineering, 3(1), 1208942. https://doi.org/10.1080/23311916.2016.1208942; Velásquez, O. S., Montoya-Giraldo, O. D., Garrido-Arévalo, V. M., & Grisales-Noreña, L. F. (2019). Optimal power flow in direct-current power grids via black hole optimization. Advances in Electrical and Electronic Engineering, 17(1), 24-32. https://doi.org/10.15598/aeee.v17i1.3069; Vélez, V. M., Hincapié, R. A., & Gallego, R. A. (2014). Low voltage distribution system planning using diversified demand curves. Electrical Power & Energy Systems, 61, 691-700. https://doi.org/10.1016/j.ijepes.2014.04.019; Winter, G. (2005). Origin of the species. Nursing Standard, 19(34), 24-26. https://doi.org/10.7748/ns.19.34.24.s28; Yadav, R., & Mahara, T. (2018). An exploratory study to investigate value chain of Saharanpur wooden carving handicraft cluster. International Journal of System Assurance Engineering and Management, 9(1), 147-154. https://doi.org/10.1007/s13198-016-0492-5; Yuan, Y., Wu, X., Wang, P., & Yuan, X. (2018). Application of improved bat algorithm in optimal power flow problem. Applied Intelligence, 48(8), 2304-2314. https://doi.org/10.1007/s10489-017-1081-2; Yuan, Z., & Hesamzadeh, M. R. (2019). Second-order cone AC optimal power flow: Convex relaxations and feasible solutions. Journal of Modern Power Systems and Clean Energy, 7(2), 268-280. https://doi.org/10.1007/s40565-018-0456-7; Zohrizadeh, F., Josz, C., Jin, M., Madani, R., Lavaei, J., & Sojoudi, S. (2020). Conic relaxations of power system optimization: Theory and algorithms. European Journal of Operational Research, 287(2), 391-409. https://doi.org/10.1016/j.ejor.2020.01.034; Zuluaga-Ríos, C. D., Florián-Ceballos, D. F., Rojo-Yepes, M. Á., & Saldarriaga-Zuluaga, S. D. (2021). Review of charging load modeling strategies for electric vehicles: A comparison of grid-to-vehicle probabilistic approaches. Tecnura, 25(70), 51-60. https://doi.org/10.14483/22487638.18657; https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/18342
Dostupnost: https://revistas.udistrital.edu.co/index.php/Tecnura/article/view/18342
-
5
Autoři: a další
Resource Type: eBook.
Categories: COMPUTERS / Artificial Intelligence / General, COMPUTERS / Programming / Algorithms, TECHNOLOGY & ENGINEERING / Engineering (General), MATHEMATICS / Optimization
Plný text ve formátu PDF Plný text ve formátu ePub -
6
Autoři:
Zdroj: Network Modeling & Analysis in Health Informatics & Bioinformatics; 11/11/2025, Vol. 14 Issue 1, p1-20, 20p
-
7
Autoři: a další
Zdroj: Hydrology & Earth System Sciences; 2025, Vol. 29 Issue 14, p3227-3256, 30p
-
8
Autoři: a další
Zdroj: Hydrological Sciences Journal/Journal des Sciences Hydrologiques; Nov2024, Vol. 69 Issue 14, p2071-2089, 19p
Témata: CLIMATE change, LAND cover, HYDROLOGIC models, RAINFALL, LAND use
-
9
Autoři: a další
Zdroj: Water (20734441); Apr2023, Vol. 15 Issue 8, p1621, 20p
-
10
Autoři: a další
Přispěvatelé: a další
Témata: 660 - Ingeniería química::661 - Tecnología de químicos industriales, 510 - Matemáticas::518 - Análisis numérico, 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores, Disolventes, Computadores electronicos digitales-diseño y construccion-procesamiento de datos, Solvents, Electronic digital computers - Design and construction - Data processing, Optimización multiobjetivo, Extracción líquido-líquido, Diseño asistido por computadora, Diseño de producto, Multi-objective optimization, Liquid-liquid extraction, Computer-aided molecular design, Product design
Popis souboru: xxxiv, 250 páginas; application/pdf
Relation: J. C. Serrato, “Diseño computacional de agentes de extracción para la separación de compuestos orgánicos en corrientes acuosas. Aplicación al ácido láctico.”, Universidad Nacional de Colombia - Sede Bogotá, 2009. doi:10.7202/1016404ar.; K. A. Rodríguez, “Inclusion of toxicity and market availability in a Computer-Aided Molecular Design methodology”, 2019.; K. Rodríguez, “Computer Aided Molecular Design : State-of-the-art and environmen- tally friendly product design”, p. 9, 2019.; E. Stefanies, L. Constantinou, y C. Panayiotou, “A group-contribution method for predicting pure component properties of biochemical and safety interest”, Ind Eng Chem Res, vol. 43, núm. 19, pp. 6253–6261, 2004, doi:10.1021/ie0497184.; M. R. Eden, S. B. Jørgensen, R. Gani, y M. M. El-Halwagi, “A novel framework for simultaneous separation process and product design”, Chemical Engineering and Processing: Process Intensification, vol. 43, núm. 5, pp. 595–608, 2004, doi:10.1016/j.cep.2003.03.002.; J. Marrero y R. Gani, “Group-contribution based estimation of pure component properties”, Fluid Phase Equilib, vol. 183–184, pp. 183–208, 2001, doi:10.1016/S0378-3812(01)00431-9.; J. Rydberg, M. Cox, C. Musikas, y G. R. Choppin, Solvent Extraction Principles and Practice, vol. 2004.; A. Tejada, R. M. Montesinos, y R. Guzmán, Bioseparaciones. 2014.; M. L. van Delden, N. J. M. Kuipers, y A. B. de Haan, “Selection and evaluation of alternative solvents for caprolactam extraction”, Sep Purif Technol, vol. 51, núm. 2, pp. 219–231, 2006, doi:10.1016/j.seppur.2006.02.003.; G. W. Meindersma, A. Podt, y A. B. De Haan, “Selection of ionic liquids for the extraction of aromatic hydrocarbons from aromatic/aliphatic mixtures”, Fuel Processing Technology, vol. 87, núm. 1, pp. 59–70, 2005, doi:10.1016/j.fuproc.2005.06.002.; K. C. Sole, “Solvent extraction in the hydrometallurgical processing and purification of metals : process design and selected applications”, en Solvent Extraction and Liquid Membranes: Fundamentals and Applications in New Materials, 2018, pp. 141–200.; N. Tik, E. Bayraktar, y U. Mehmetoglu, “In situ reactive extraction of lactic acid from fermentation media”, Journal of Chemical Technology and Biotechnology, vol. 76, núm. 7, pp. 764–768, 2001, doi:10.1002/jctb.449.; A. B. de Haan, “Affinity solvents for intensified organics extraction: Development challenges and prospects”, Tsinghua Sci Technol, vol. 11, núm. 2, pp. 171–180, 2006, doi:10.1016/S1007-0214(06)70172-9.; W. L. McCabe, J. C. Smith, y P. Harriott, Operaciones unitarias en Ingeniería Química. 1991.; J. Gmehling, M. Kleiber, B. Kolbe, y J. Rarey, Chemical Thermodynamics for Process Simulation. 2019. doi:10.1002/9783527809479.; J. M. Smith, H. C. van Ness, y M. M. Abott, Introducción a la Termodinámica en Ingeniería Química. 2007. [En línea]. Disponible en: http://librosysolucionarios.net/; R. E. Treybal, “Azeotropes”, A-to-Z Guide to Thermodynamics, Heat and Mass Transfer, and Fluids Engineering, 2008, doi:10.1615/atoz.a.azeotropes.; M. C. M. Cockrem, E. N. Lightfoot, y J. H. Flatt, “Solvent Selection for Extraction from Dilute Solution”, Sep Sci Technol, vol. 24, núm. 11, pp. 769–807, 1989, doi:10.1080/01496398908049876.; J. Medina-Mora, “Optimización Multiobjetivo para la selección de solventes, aplicable a la Extracción Líquido-Líquido”, 2010.; E. Brignole y S. Pereda, Phase equilibrium engineering principles, vol. 3. 2013. doi:10.1016/B978-0-444-56364-4.00006-6.; L. E. K. Achenie, R. Gani, y V. Venkatasubramanian, Computer Aided Molecular Design: Theory and Practice. 2003.; N. G. Martín, Mariano Eden, Mario R. Chemmangattuvalappil, Tools for Chemical Product Design. 2017.; H. Fruehbeis, R. Klein, y H. Wallmeier, “Computer-Assisted Molecular Design (CAMD) - An Overview”, ChemInform, vol. 18, núm. 35, pp. 403–418, 1987, doi:10.1002/chin.198735397.; L. A. Cisternas y E. D. Gálvez, “Principles for chemical products design”, Computer Aided Chemical Engineering, vol. 21, núm. C, pp. 1107–1112, 2006, doi:10.1016/S1570-7946(06)80194-X.; G. D. Moggridge y E. L. Cussler, “An introduction to chemical product design”, Chemical Engineering Research and Design, vol. 78, núm. 1, pp. 5–11, 2000, doi:10.1205/026387600527022.; A. K. Tula, D. K. Babi, J. Bottlaender, M. R. Eden, y R. Gani, “A computer-aided software-tool for sustainable process synthesis-intensification”, Comput Chem Eng, vol. 105, pp. 74–95, 2017, doi:10.1016/j.compchemeng.2017.01.001.; L. Y. Ng, N. G. Chemmangattuvalappil, V. A. Dev, y M. R. Eden, Mathematical Principles of Chemical Product Design and Strategies, vol. 39. 2016. doi:10.1016/B978-0-444-63683-6.00001-0.; R. Gani, “Chemical product design: Challenges and opportunities”, Comput Chem Eng, vol. 28, núm. 12, pp. 2441–2457, 2004, doi:10.1016/j.compchemeng.2004.08.010.; L. Y. Ng, F. K. Chong, y N. G. Chemmangattuvalappil, “Challenges and opportunities in computer-aided molecular design”, Comput Chem Eng, vol. 81, pp. 115–129, 2015, doi:10.1016/j.compchemeng.2015.03.009.; S. S. Y. Wong, W. Luo, y K. C. C. Chan, “EvoMD: An algorithm for evolutionary molecular design”, IEEE/ACM Trans Comput Biol Bioinform, vol. 8, núm. 4, pp. 987–1003, 2011, doi:10.1109/TCBB.2010.100.; J. Scheffczyk, L. Fleitmann, A. Schwarz, M. Lampe, A. Bardow, y K. Leonhard, “COSMO-CAMD: A framework for optimization-based computer-aided molecular design using COSMO-RS”, Chem Eng Sci, vol. 159, pp. 84–92, 2017, doi:10.1016/j.ces.2016.05.038.; C. C. Solvason, “Integrated Multiscale Chemical Product Design using Property Clustering and Decomposition Techniques in a Reverse Problem Formulation”, 2011.; R. Gani, L. E. K. Achenie, y V. Venkatasubramanian, “Chapter 1: Introduction to CAMD”, en Computer Aided Molecular Design: Theory and Practice, 2003, pp. 3–21.; N. D. Austin, N. V. Sahinidis, y D. W. Trahan, “Computer-aided molecular design: An introduction and review of tools, applications, and solution techniques”, Chemical Engineering Research and Design, vol. 116, núm., pp. 2–26, 2016, doi:10.1016/j.cherd.2016.10.014.; M. Harini, J. Adhikari, y K. Y. Rani, “A review on property estimation methods and computational schemes for rational solvent design: A focus on pharmaceuticals”, Ind Eng Chem Res, vol. 52, núm. 21, pp. 6869–6893, 2013, doi:10.1021/ie301329y.; P. M. Harper, R. Gani, P. Kolar, y T. Ishikawa, “Computer-aided molecular design with combined molecular modeling and group contribution”, Fluid Phase Equilib, vol. 158–160, pp. 337–347, 1999, doi:10.1016/s0378-3812(99)00089-8.; T. Martin, User’s guide for T.E.S.T. (version 4.2) (Toxicity Estimation Software Tool) A program to estimate toxicity from molecular structure. 2016, p. 63.; J. Song y H. H. Song, “Computer-aided molecular design of environmentally friendly solvents for separation processes”, Chem Eng Technol, vol. 31, núm. 2, pp. 177–187, 2008, doi:10.1002/ceat.200700233.; J. Y. Ten, M. H. Hassim, D. K. S. Ng, y N. G. Chemmangattuvalappil, “The Incorporation of Safety and Health Aspects as Design Criteria in a Novel Chemical Product Design Framework”, Computer Aided Chemical Engineering, vol. 39, pp. 197–220, 2016, doi:10.1016/B978-0-444-63683-6.00007-1.; R. Gani, B. Nielsen, y A. Fredenslund, “A group contribution approach to computer‐aided molecular design”, AIChE Journal, vol. 37, núm. 9, pp. 1318–1332, 1991, doi:10.1002/aic.690370905.; O. Odele y S. Macchietto, “Computer Aided Molecular Design: A Novel Method for Optimal Solvent Selection”, Fluid Phase Equilib, vol. 00226020, núm. 3, pp. 47–54, 1993.; P. M. Harper y R. Gani, “A multi-step and multi-level approach for computer aided molecular design”, Comput Chem Eng, vol. 24, núm. 2–7, pp. 677–683, 2000, doi:10.1016/S0098-1354(00)00410-5.; B. C. Roughton, “Development of Computer-Aided Molecular Design Methods for Bioengineering Applications”, 2013.; S. Cignitti, I. Rodriguez-Donis, J. Abildskov, X. You, N. Shcherbakova, y V. Gerbaud, “CAMD for entrainer screening of extractive distillation process based on new thermodynamic criteria”, Chemical Engineering Research and Design, vol. 147, pp. 721–733, 2019, doi:10.1016/j.cherd.2019.04.038.; B. van Dyk y I. Nieuwoudt, “Design of solvents for extractive distillation”, Ind Eng Chem Res, vol. 39, núm. 5, pp. 1423–1429, 2000, doi:10.1021/ie9904753.; N. Medina-Herrera, I. E. Grossmann, M. S. Mannan, y A. Jiménez-Gutiérrez, “An approach for solvent selection in extractive distillation systems including safety considerations”, Ind Eng Chem Res, vol. 53, núm. 30, pp. 12023–12031, 2014, doi:10.1021/ie501205j.; S. Kossack, K. Kraemer, R. Gani, y W. Marquardt, “A systematic synthesis framework for extractive distillation processes”, Chemical Engineering Research and Design, vol. 86, núm. 7, pp. 781–792, 2008, doi:10.1016/j.cherd.2008.01.008.; T. Zhou, Z. Song, X. Zhang, R. Gani, y K. Sundmacher, “Optimal Solvent Design for Extractive Distillation Processes: A Multiobjective Optimization-Based Hierarchical Framework”, Ind Eng Chem Res, vol. 58, núm. 15, pp. 5777–5786, 2019, doi:10.1021/acs.iecr.8b04245.; J. Sun, H. Zhang, A. Zhou, Q. Zhang, y K. Zhang, “A new learning-based adaptive multi-objective evolutionary algorithm”, Swarm Evol Comput, vol. 44, núm. December 2017, pp. 304–319, 2019, doi:10.1016/j.swevo.2018.04.009.; A. T. Karunanithi, C. Acquah, L. E. K. Achenie, S. Sithambaram, y S. L. Suib, “Solvent design for crystallization of carboxylic acids”, Comput Chem Eng, vol. 33, núm. 5, pp. 1014–1021, 2009, doi:10.1016/j.compchemeng.2008.11.003.; V. Venkatasubramanian, K. Chan, y J. M. Caruthers, “Evolutionary Design of Molecules with Desired Properties Using the Genetic Algorithm”, J Chem Inf Comput Sci, vol. 35, núm. 2, pp. 188–195, 1995, doi:10.1021/ci00024a003.; M. Mattei, M. Hill, G. M. Kontogeorgis, y R. Gani, “Design of an emulsion-based personal detergent through a model-based chemical product design methodology”, Computer Aided Chemical Engineering, vol. 32, pp. 817–822, 2013, doi:10.1016/B978-0-444-63234-0.50137-8.; R. Gani, “Chapter 14 Case studies in chemical product design - use of CAMD techniques”, Computer Aided Chemical Engineering, vol. 23, núm. 1991, pp. 435–458, 2007, doi:10.1016/S1570-7946(07)80017-4.; K. Zhou Teng; Wang, Jiayuan; Mcbride, Kevin; Sundmacher, “Optimal Design of Solvents for Extractive Reaction Process”, AICHE Journal, vol. 61, núm. 3, pp. 857–866, 2015, doi:10.1002/aic.; M. Skiborowski, “Process synthesis and design methods for process intensification”, Curr Opin Chem Eng, vol. 22, pp. 216–225, 2018, doi:10.1016/j.coche.2018.11.004.; S. J. Patel, “Integrating Safety Issues in Optimizing Solven Selection and Porcess Design”, 2010.; J. Ooi, D. K. S. Ng, y N. G. Chemmangattuvalappil, “A Systematic Molecular Design Framework with the Consideration of Competing Solvent Recovery Processes”, Ind Eng Chem Res, vol. 58, núm. 29, pp. 13210–13226, 2019, doi:10.1021/acs.iecr.9b01894.; J. E. Ourique y A. Silva Telles, “Computer-aided molecular design with simulated annealing and molecular graphs”, Comput Chem Eng, vol. 22, núm. SUPPL.1, pp. 0–3, 1998, doi:10.1016/s0098-1354(98)00108-2.; J. Heintz, J. P. Belaud, N. Pandya, M. Teles Dos Santos, y V. Gerbaud, “Computer aided product design tool for sustainable product development”, Comput Chem Eng, vol. 71, pp. 362–376, 2014, doi:10.1016/j.compchemeng.2014.09.009.; J. Heintz, “Systemic approach and decision process for sustainability in chemical engineering: Applcation to computer aided product design. PhD Thesis”, p. 256, 2012.; J. Y. Ten, M. H. Hassim, D. K. S. Ng, y N. G. Chemmangattuvalappil, “A molecular design methodology by the simultaneous optimisation of performance, safety and health aspects”, Chem Eng Sci, vol. 159, pp. 140–153, 2017, doi:10.1016/j.ces.2016.03.026.; J. Devillers, Genetic Algorithms in Molecular Modeling, vol., núm. June. 2016. doi:10.1016/b978-0-12-213810-2.x5000-2.; B. Lin, S. Chavali, K. Camarda, y D. C. Miller, “Computer-aided molecular design using Tabu search”, Comput Chem Eng, vol. 29, núm. 2, pp. 337–347, 2005, doi:10.1016/j.compchemeng.2004.10.008.; A. S. Hukkerikar, B. Sarup, A. Ten Kate, J. Abildskov, G. Sin, y R. Gani, “Group-contribution+ (GC+) based estimation of properties of pure components: Improved property estimation and uncertainty analysis”, Fluid Phase Equilib, vol. 321, pp. 25–43, 2012, doi:10.1016/j.fluid.2012.02.010.; A. S. Hukkerikar, S. Kalakul, B. Sarup, D. M. Young, G. Sin, y R. Gani, “Estimation of environment-related properties of chemicals for design of sustainable processes: Development of group-contribution+ (GC +) property models and uncertainty analysis”, J Chem Inf Model, vol. 52, núm. 11, pp. 2823–2839, 2012, doi:10.1021/ci300350r.; A. S. Hukkerikar, G. Sin, J. Abildskov, B. Sarup, y R. Gani, Development of pure component property models for chemical product-process design and analysis, núm. September. 2013.; R. L. Haupt y S. E. Haupt, Pratical Genetic Algotithms, vol. 2004.; R. Kumar, Optimization: Algorithms and Applications, vol. CRC Press, 2015.; Z. Dostál, Optimal Quadratic Programming Algorithms: With Applications to Variational Inequalities, vol. 23. 2009. doi:10.1007/b138610.; J. A. Snyman y D. N. Wilke, Practical Mathematical Optimization, vol. 133. Cham, Switzerland, 2005. doi:10.1007/b105200.; K.-H. Chang, “Chapter 3 - Design Optimization”, en Design Theory and Methods Using CAD/CAE, 2015, pp. 103–210. doi:10.1080/03772063.2020.1842159.; J. A. Caballero y I. E. Grossmann, “A review of the state of the art in optimization”, RIAI - Revista Iberoamericana de Automatica e Informatica Industrial, vol. 4, núm. 1, pp. 5–23, 2007, doi:10.1016/s1697-7912(07)70188-7.; X.-S. Yang, Nature-Inspired Optimization Algorithms. 2020.; C. A. C. Coello, “Introduccion a la Computacion Evolutiva”, núm. 16, p. 310, 2004.; M. A. Iglesias-Solano y A. B. Iglesias-Carbonell, “La Computación Evolutiva y sus Paradigmas Paradigms of Evolutionary Computing”, Investigación y Desarrollo en TIC, vol. 2, pp. 29–38, 2011.; N. Yusup, A. M. Zain, y S. Z. M. Hashim, “Evolutionary techniques in optimizing machining parameters: Review and recent applications (2007-2011)”, Expert Syst Appl, vol. 39, núm. 10, pp. 9909–9927, 2012, doi:10.1016/j.eswa.2012.02.109.; A. Menon, Frontiers of Evolutionary Computation, vol., núm. 2004.; C. J. Correa-Villalón, “Diseño de un Algoritmo Evolutivo para atacar Problemas NP-Duros basado en la Técnica Transgénicas”, Universidad Autónoma de Aguascalientes (México), 2010. doi: -.; D. E. Goldberg, “Genetic Algorithms in Search Optimization & Machine Learning”. p. 432, 1989.; J. H. Holland, “Genetic Algorithms - Computer programs that ‘evolve’ in ways that resemble natural selection can solve complex problems even their creators do not fully understand”, Scientific American. pp. 66–72, 1992.; J. H. Holland, C. Langton, y S. W. Wilson, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. 1992.; M. Pelikan, Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms, vol. 53, núm. 9. 2013. doi:10.1017/CBO9781107415324.004.; T. Bäck, “Evolutionary algorithms in theory and practice: Evolution strategies, evolutionary programming, genetic algorithms”. p. 315, 1996.; Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, vol. 1, núm. 1996.; D. A. Coley, “An Introduction to Genetic Algorithms for Scientists and Engineers”. p. 185, 1999.; S. Kumar y P. J. Bentley, “Biologically Inspired Evolutionary Development”, ICES, vol., núm., pp. 57–68, 2003, doi:10.1007/3-540-36553-2_6.; D. Vasiljević, Classical and Evolutionary Algorithms in the Optimization of Optical Systems, vol., núm. 2006.; M. Melanie, An introduction to genetic algorithms, vol. 1999. doi:10.1016/S0898-1221(96)90227-8.; S. N. Sivanandam y S. N. Deepa, Introduction to Genetic Algorithms, vol. 2008.; D. Simon, Evolutionary Optimization Aglorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence, vol., núm. John Wiley & Sons, Inc., 2013.; W.-H. Steeb, The nonlinear workbook: Chaos, Fractals, Celular Automata, Neural Networks, Genetic Algorithms, Fuzzy Logic with C++, Java, SymbolicC++ and Reduce Programs, vol. 1999.; J. Branke, Evolutionary optimization in dynamic environment, vol. 2002. doi:10.1109/ICCP.2009.5284794.; C. B. Lucasius y G. Kateman, “Understanding and using genetic algorithms Part 2. Representation, configuration and hybridization”, Chemometrics and Intelligent Laboratory Systems, vol. 25, pp. 99–145, 1994.; F. Rothlauf, Representations for Genetic and Evolutionary Algorithms, vol. 2006.; F. Corno, M. Sonza Reorda, y G. Squillero, “VEGA: a verification tool based on genetic algorithms”, Proceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors, pp. 321–326, 1998, doi:10.1109/iccd.1998.727069.; N. Srinivas y K. Deb, “Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms”, núm. 0, 1386.; K. Deb, S. Agrawal, A. Pratap, y T. Meyarivan, “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1917, pp. 849–858, 2000, doi:10.1007/3-540-45356-3_83.; T. Murata y H. Ishibuchi, “MOGA: multi-objective genetic algorithms”, Proceedings of the IEEE Conference on Evolutionary Computation, vol. 1, pp. 289–294, 1995, doi:10.1109/icec.1995.489161.; E. Zitzler y L. Thiele, “Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach”, vol. 3, núm. 4, pp. 257–271, 1999.; E. Zitzler, M. Laumanns, y L. Thiele, “SPEA2: Improving the Strength Pareto Evolutionary Algorithm”, TIK-Report 103 May, pp. 1–21, 2001, doi:10.1007/978-3-319-11119-3_4.; J. Gomez, “Self adaptation of operator rates in evolutionary algorithms”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3102, pp. 1162–1173, 2004, doi:10.1007/978-3-540-24854-5_113.; J. Gómez, “Hybrid Adaptive Evolutionary Algorithm Hyper Heuristic”, pp. 1–5, 2014.; C. R. Reeves y J. E. Rowe, Genetic algorithms: Principles and Perspectives - A Guide to GA Theory. 2002.; S. Bandyopadhyay y S. K. Pal, Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence, vol. 2007.; L. Chambers, Practical Handbook of Genetic Algorithms - New Frontiers Volume II. 1995.; D. B. Hibbert, “Genetic algorithms in Chemistry”, Chemometrics and Intelligent Laboratory Systems, vol. 19, núm. 8 SPEC., pp. 277–293, 1993, doi:10.1016/S0010-4485(03)00002-2.; L. Elliott, D. B. Ingham, A. G. Kyne, N. S. Mera, M. Pourkashanian, y C. W. Wilson, “Genetic algorithms for optimisation of chemical kinetics reaction mechanisms”, Progress in Enery and Combustion Science, vol. 30, pp. 297–328, 2004, doi:10.1016/j.pecs.2004.02.002.; C. B. Lucasius y G. Kateman, “Understanding and using genetic algorithms Part 1. Concepts, properties and context”, Chemometrics and Intelligent Laboratory Systems, vol. 19, núm. 1, pp. 1–33, 1993, doi:10.1016/0169-7439(93)80079-W.; L. Gosselin, M. Tye-Gingras, y F. Mathieu-Potvin, “Review of utilization of genetic algorithms in heat transfer problems”, Int J Heat Mass Transf, vol. 52, núm. 9–10, pp. 2169–2188, 2009, doi:10.1016/j.ijheatmasstransfer.2008.11.015.; J. G. Andreasen, U. Larsen, T. Knudsen, L. Pierobon, y F. Haglind, “Selection and optimization of pure and mixed working fluids for low grade heat utilization using organic rankine cycles”, Energy, vol. 73. pp. 204–213, 2014. doi:10.1016/j.energy.2014.06.012.; Z. Kravanja y M. Bogataj, “26 European Symposium on Computer Aided Process Engineering-Elsevier”, Computer Aided Chemical Engineering, vol. 38, p. 588, 2016.; M. Fan, J. Hu, R. Cao, W. Ruan, y X. Wei, “A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence”, Chemosphere, vol. 200, pp. 330–343, 2018, doi:10.1016/j.chemosphere.2018.02.111.; A. Maiocchi, “Genetic algorithms in molecular modelling: a review”, en Data Handling in Science and Technology, 2003, pp. 109–139. doi:10.1016/S0922-3487(03)23004-5.; T. Lisboa, “Multi-Objective Optimization”, Técnico Lisboa. pp. 148–173.; A. Ramos, “Optimización Multicriterio”, Universidad Pontificia Comillas, núm. p. 16.; C. A. C. Coello, “Aplicaciones de los Algoritmos Evolutivos Multiobjetivo”, núm. 2508, 2012.; H. Massam, “Multi-criteria (MCDM) Decision Making Techniques in Planning”, Prog Plann, vol. 30, núm. Mcdm, pp. 1–84, 1988.; P. L. Yu, “Multiple criteria decision making: Five basic concepts”, Handbooks in Operations Research and Management Science, vol. 1, núm. C, pp. 663–699, 1989, doi:10.1016/S0927-0507(89)01011-X.; C. A. C. Coello, G. B. Lamont, y D. A. Van Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems, vol. 2007. doi:10.1080/00949659608811725.; S. Chand y M. Wagner, “Evolutionary many-objective optimization: A quick-start guide”, Surveys in Operations Research and Management Science, vol. 20, núm. 2, pp. 35–42, 2015, doi:10.1016/j.sorms.2015.08.001.; R. T. Marler y J. S. Arora, “Survey of multi-objective optimization methods for engineering”, Structural and Multidisciplinary Optimization, vol. 26, núm. 6, pp. 369–395, 2004, doi:10.1007/s00158-003-0368-6.; C. Zopounidis y P. M. Pardalos, Handbook of Multicriteria Analysis. 2010.; D. R. Insua, “Sobre soluciones optimas en problemas de optimizacion multiobjetivo”, Trabajos de Investigacion Operativa, vol. 2, núm. 1, pp. 49–67, 1987, doi:10.1007/BF02888810.; C. M. Subía, “Desarrollo de una Guía Metodologíca sobre Computación Evolutiva y Algoritmos Genéticos, para la Optimiziación Evolutiva Multiobjetivo”, 2014. doi:10.4324/9781315853178.; S. Hernández, “Del diseño convencional al diseño óptimo. Posibilidades y variantes”, Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, vol. 9, pp. 259–270, 1993.; H. Mukai, “Algorithms for Multicriterion Optimization”, IEEE Trans Automat Contr, vol. 25, núm. 2, pp. 177–186, 1980, doi:10.1109/TAC.1980.1102298.; G. Giorgi, B. Jiménez, y V. Novo, “Approximate Karush–Kuhn–Tucker Condition in Multiobjective Optimization”, J Optim Theory Appl, vol. 171, núm. 1, pp. 70–89, 2016, doi:10.1007/s10957-016-0986-y.; E. Mezura-Montes y C. A. Coello Coello, “Conceptos de Optimización Multiobjetivo para el Manejo de Restricciones en Algoritmos Evolutivos: Un Estudio Comparativo”, Proceedings of the 1st Mexican Conference on Evolutionary Computation (COMCEV 2003), pp. 1–12, 2003.; N. Riquelme, C. von Lücken, y B. Barán, “Performace metrics in multi-objective optimization”, en 2015 XLI Latin American Computing Conference (CLEI) Performance, 2015.; B. A. Cuartas Torres, “Metodología para la optimización de múltiples objetivos basada en ag y uso de preferencias”, Universidad Nacional de Colombia - Sede Medellín, 2009. [En línea]. Disponible en: http://www.bdigital.unal.edu.co/2237/%5Cnhttp://www.bdigital.unal.edu.co/2237/1/43908352.2009.pdf; N. Riquelme, C. Von Lücken, y B. Barán, “Performance metrics in multi-objective optimization”, Proceedings - 2015 41st Latin American Computing Conference, CLEI 2015, vol. 1, p. 11, 2015, doi:10.1109/CLEI.2015.7360024.; J. A. Párraga, “Clustering difuso multi-objetivo de genes basado en información biológica externa y datos de expresión génica”, Universidad de Santiago de Chile, 2017. doi:10.1088/1742-6596/134/1/012001.; J. C. Castro, “Modelo de optimización multiobjetivo para el algoritmo evolutivo HAEA (Hybrid Adaptative Evolutionary Algoritm)”, Universidad Nacional de Colombia, 2020.; K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, vol. John Wiley & Sons, 2001.; R. T. Marler y J. S. Arora, “The weighted sum method for multi-objective optimization: New insights”, Structural and Multidisciplinary Optimization, vol. 41, núm. 6, pp. 853–862, 2010, doi:10.1007/s00158-009-0460-7.; A. Singh y S. Kumar, “Multiple Objectives Mathematical Programming Using Payoff Techniques”, vol. 9, núm. 1, pp. 39–46, 2012.; S. Obayashi, D. Sasaki, y A. Oyama, “Finding tradeoffs by using multiobjective optimization algorithms”, Trans Jpn Soc Aeronaut Space Sci, vol. 47, núm. 155, pp. 51–58, 2004, doi:10.2322/tjsass.47.51.; M. Sakawa, Genetic algorithms and fuzzy multiobjective optimization, vol. 1, núm. 2002.; R. Wang, R. C. Purshouse, y P. J. Fleming, “Preference-inspired co-evolutionary algorithms using weight vectors”, Eur J Oper Res, vol. 243, núm. 2, pp. 423–441, 2015, doi:10.1016/j.ejor.2014.05.019.; W. Wang, S. Ying, L. Li, Z. Wang, y W. Li, “An improved decomposition-based multiobjective evolutionary algorithm with a better balance of convergence and diversity”, Applied Soft Computing Journal, vol. 57, pp. 627–641, 2017, doi:10.1016/j.asoc.2017.03.041.; Q. Zhang, W. Zhu, B. Liao, X. Chen, y L. Cai, “A modified PBI approach for multi-objective optimization with complex Pareto fronts”, Swarm Evol Comput, vol. 40, núm. February, pp. 216–237, 2018, doi:10.1016/j.swevo.2018.02.001.; C. A. C. Coello, “Introducción a la Optimización Evolutiva Multiobjetivo”, Apuntes de clase: Introducción a la optimización multiobjetivo., vol., núm. 4. pp. 1–144, 2012.; J. D. Schaffer, “Multiple objective optimization with vector evaluated genetic algorithms”, The 1st international Conference on Genetic Algorithms, núm. JANUARY 1985, pp. 93–100, 1985.; K. Deb, A. Pratap, S. Agarwal, y T. Meyarivan, “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II”, IEEE Transactions on Evolutionary Computation, vol. 6, núm. 2, pp. 182–197, 2002, doi:10.1109/4235.996017.; C. A. Correa Flórez, R. Andrés Bolaños, y A. Molina Cabrera, “Algoritmo multiobjetivo NSGA-II aplicado al problema de la mochila.”, Scientia Et Technica, vol. 2, núm. 39, pp. 206–211, 2008.; L. Lopez, R. A. Hincapié, y R. A. Gallego, “Planeamiento multi-objetivo de sistemas de distribución usando un algoritmo evolutivo NSGA-II”, Revista Escuela de Ingeniería de Antioquía, vol. 15, núm. 15, pp. 141–151, 2011.; N. Dahmani, F. Clautiaux, S. Krichen, y E. G. Talbi, “Self-adaptive metaheuristics for solving a multi-objective 2-dimensional vector packing problem”, Applied Soft Computing Journal, vol. 16, pp. 124–136, 2014, doi:10.1016/j.asoc.2013.12.006.; L. Wang, A. H. C. Ng, y K. Deb, “Multi-objective Evolutionary Optimisation for Product Design and Manufacturing”, Assembly Automation, vol. 32, núm. 4, pp. 142–147, 2012, doi:10.1108/aa.2012.03332daa.009.; J. Gomez, “Self adaptation of operator rates for multimodal optimization”, Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004, vol. 2, pp. 1720–1726, 2004, doi:10.1109/cec.2004.1331103.; GECCO ’19, “MOHAEA: A Multi-objective Hybrid Adaptive Evolutionary Algorithm”, en GECCO ’19, July 13–17, 2019, 2019, pp. 1–3.; J. Horn, N. Nafpliotis, y D. E. Goldberg, “A niched Pareto genetic algorithm for multiobjective optimization”, en First IEEE Conference on Evolutionary Computation, 2002, pp. 82–87. doi:10.1109/icec.1994.350037.; C.-L. Hwang, Y.-J. Lai, y T.-Y. Liu, “A New Approach for Multiple Objective Decision Making”, Comput Oper Res, vol. 20, núm. 8, pp. 889–899, 1993.; J. G. Vlachogiannis y K. Y. Lee, “Multi-objective based on parallel vector evaluated particle swarm optimization for optimal steady-state performance of power systems”, Expert Syst Appl, vol. 36, núm. 8, pp. 10802–10808, 2009, doi:10.1016/j.eswa.2009.02.079.; L. C. Cagnina, “Optimización Mono y Multiobjetivo a través de una Heurística de Inteligencia Colectiva”, pp. 54–72, 2010.; A. Lara López, “Un estudio de las Estrategias Evolutivas para problemas Multiobjetivo.”, pp. 23–25, 2003.; N. A. Ramírez, “Una nueva propuesta para optimización multiobjetivo basada en búsqueda dispersa (Scatter Search)”, 2006.; M. Gul, E. Celik, N. Aydin, A. Taskin Gumus, y A. F. Guneri, “A state of the art literature review of VIKOR and its fuzzy extensions on applications”, Applied Soft Computing Journal, vol. 46, pp. 60–89, 2016, doi:10.1016/j.asoc.2016.04.040.; Y.-Z. Lu, Y.-W. Chen, M.-R. Chen, P. Chen, y G.-Q. Zeng, Extremal Optimization: Fundamentals, Algorithms, and Applications, vol. 2016.; S. S. Santander-Jiménez, M. A. Vega-Rodríguez, J. A. Gómez-Pulido, y J. M. Sánchez-Pérez, “Una adaptación multiobjetivo y paralela del algoritmo Artificial Bee Colony aplicada a la inferencia filogenética”, 1996.; C.-K. Goh y K. C. Tan, Evolutionary Multi-objective Optimization in Uncertain Environments: Issues and Algorithms, vol. 186. 2009. doi:10.1007/978-3-540-95976-2.; M. Macías Infantes, “Estudio Comparativo de Técnicas de Optimización para la Actualización de Modelos de Elementos Finitos”, 2016.; L. C. Jain y N. M. Martin, Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms: Industrial Applications, vol. 1. 1998.; G. Toscano, “Optimización Multiobjetivo usando un Micro Algoritmo Genético”, Universidad Veracurzana - LANIA, 2001.; C. Almeida, N. Amarilla, y B. Barán, “Optimización Multiobjetivo en la Planificación de Centrales Telefónicas”, 2003.; C. Castillo, “Aplicación de la Programacion Multiobjetivo en la Optimización del Tráfico Generado por un IDS/IPS”, Rev. Tecnol. - Journal of Technology, vol. 11, núm. 1, pp. 41–55, 2012.; S. Ruiz, O. D. Castrillón, y W. A. Sarache, “Una metodología multiobjetivo para optimizar un ambiente job shop”, Informacion Tecnologica, vol. 23, núm. 1, pp. 35–46, 2012, doi:10.4067/S0718-07642012000100005.; M. Guzek, J. E. Pecero, B. Dorronsoro, y P. Bouvry, “Multi-objective evolutionary algorithms for energy-aware scheduling on distributed computing systems”, Applied Soft Computing Journal, vol. 24, pp. 432–446, 2014, doi:10.1016/j.asoc.2014.07.010.; C. Carnevale, G. Finzi, E. Pisoni, y M. Volta, “Multi-objective analysis to control ozone exposure”, en Developments in Environmental Science, 2007, pp. 96–108. doi:10.1016/S1474-8177(07)06023-8.; P. S. Moura y A. T. de Almeida, “Multi-objective optimization of a mixed renewable system with demand-side management”, Renewable and Sustainable Energy Reviews, vol. 14, núm. 5, pp. 1461–1468, 2010, doi:10.1016/j.rser.2010.01.004.; M. J. Bastidas, R. F. Bermúdez, G. P. Jaramillo, y F. Chejne, “Optimización termoeconómica y ambiental usando algoritmos genéticos multiobjetivo”, Informacion Tecnologica, vol. 21, núm. 4, pp. 35–44, 2010, doi:10.1612/inf.tecnol.4384it.09.; P. J. Copado-Méndez, C. Pozo, G. Guillén-Gosálbez, y L. Jiménez, “Enhancing the ε-constraint method through the use of objective reduction and random sequences: Application to environmental problems”, Comput Chem Eng, vol. 87, pp. 36–48, 2016, doi:10.1016/j.compchemeng.2015.12.016.; M. N. Naz, M. I. Mushtaq, M. Naeem, M. Iqbal, M. W. Altaf, y M. Haneef, “Multicriteria decision making for resource management in renewable energy assisted microgrids”, Renewable and Sustainable Energy Reviews, vol. 71, núm. December 2016, pp. 323–341, 2017, doi:10.1016/j.rser.2016.12.059.; R. T. F. Ah. King, K. Deb, y H. C. S. Rughooputh, “Comparison of NSGA-II and SPEA2 on the Multiobjective Environmental/Economic Dispatch Problem”, University of Mauritius Research Journal, vol. 16, núm. 1, pp. 485–511, 2010.; L. Atmaniou et al., “A multiobjective genetic algorithm optimization framework for batch plant design”, Computer Aided Chemical Engineering, vol. 15, núm. C, pp. 400–405, 2003, doi:10.1016/S1570-7946(03)80577-1.; C. Gutérrez-Antonio, A. Briones-Ramírez, y A. Jiménez-Gutiérrez, “Optimization of Petlyuk sequences using a multi objective genetic algorithm with constraints”, Comput Chem Eng, vol. 35, núm. 2, pp. 236–244, 2011, doi:10.1016/j.compchemeng.2010.10.007.; A. I. Papadopoulos y P. Linke, “Multiobjective molecular design for integrated process-solvent systems synthesis”, AIChE Journal, vol. 52, núm. 3, pp. 1057–1070, 2006, doi:10.1002/aic.10715.; S. Ekins, J. D. Honeycutt, y J. T. Metz, “Evolving molecules using multi-objective optimization: Applying to ADME/Tox”, Drug Discov Today, vol. 15, núm. 11–12, pp. 451–460, 2010, doi:10.1016/j.drudis.2010.04.003.; L. Y. Ng, N. G. Chemmangattuvalappil, y D. K. S. Ng, “A multiobjective optimization-based approach for optimal chemical product design”, Ind Eng Chem Res, vol. 53, núm. 44, pp. 17429–17444, 2014, doi:10.1021/ie502906a.; P. Bigus, J. Namieśnik, y M. Tobiszewski, “Application of multicriteria decision analysis in solvent type optimization for chlorophenols determination with a dispersive liquid-liquid microextraction”, J Chromatogr A, vol. 1446, pp. 21–26, 2016, doi:10.1016/j.chroma.2016.03.065.; C. M. Fonseca y P. J. Fleming, “Genetic Algorithms for Multi-Objective Optimization: Formulation, discussion and generalization”, en Proceedings of the 5th International Conference on Genetic Algorithms, 1993, pp. 416–423. doi:10.3156/jfuzzy.9.4_471_1.; M. Garza-Fabre, G. T. Pulido, y C. A. C. Coello, “Ranking methods for many-objective optimization”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5845 LNAI, pp. 633–645, 2009, doi:10.1007/978-3-642-05258-3_56.; K. Deb y H. Jain, “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, vol. 18, núm. 4, pp. 577–601, 2014, doi:10.1109/TEVC.2013.2281535.; H. Ishibuchi, N. Tsukamoto, y Y. Nojima, “Evolutionary many-objective optimization: A short review”, 2008 IEEE Congress on Evolutionary Computation, CEC 2008, pp. 2419–2426, 2008, doi:10.1109/CEC.2008.4631121.; H. Sato, “Pareto Partial Dominance MOEA in Many-Objective Optimization”, Search (Syd), núm. January, pp. 1–10, 2009.; H. Aguirre y K. Tanaka, “Many-objective optimization by space partitioning and adaptive ∈-ranking on MNK-landscapes”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5467 LNCS, pp. 407–422, 2010, doi:10.1007/978-3-642-01020-0_33.; C. H. Papadimitriou y M. Yannakakis, “On the approximability of trade-offs and optimal access of web sources”, Annual Symposium on Foundations of Computer Science - Proceedings, pp. 86–92, 2000, doi:10.1109/sfcs.2000.892068.; T. Erlebach, H. Kellerer, y U. Pferschy, “Approximating Multi-objective Knapsack Problems”, pp. 210–211, 2001.; J. Bader y E. Zitzler, “HypE : An algorithm for fast optimization”, Evol Comput, vol. 19, núm. 1, pp. 45–76, 2011.; L. While, P. Hingston, L. Barone, y S. Huband, “A faster algorithm for calculating hypervolume”, IEEE Transactions on Evolutionary Computation, vol. 10, núm. 1, pp. 29–38, 2006, doi:10.1109/TEVC.2005.851275.; K. Bringmann y T. Friedrich, “Approximating the volume of unions and intersections of high-dimensional geometric objects”, Comput Geom, vol. 43, núm. 6–7, pp. 601–610, 2010, doi:10.1016/j.comgeo.2010.03.004.; X. Cai, H. Sun, y Z. Fan, “A diversity indicator based on reference vectors for many-objective optimization”, Inf Sci (N Y), vol. 430–431, pp. 467–486, 2018, doi:10.1016/j.ins.2017.11.051.; G. Dai, C. Zhou, M. Wang, y X. Li, “Indicator and reference points co-guided evolutionary algorithm for many-objective optimization problems”, Knowl Based Syst, vol. 140, pp. 50–63, 2018, doi:10.1016/j.knosys.2017.10.025.; M. Zhang y H. Li, “A reference direction and entropy based evolutionary algorithm for many-objective optimization”, Applied Soft Computing Journal, vol. 70, pp. 108–130, 2018, doi:10.1016/j.asoc.2018.05.011.; J. Zou, C. Ji, S. Yang, Y. Zhang, J. Zheng, y K. Li, “A knee-point-based evolutionary algorithm using weighted subpopulation for many-objective optimization”, Swarm Evol Comput, vol. 47, núm. January, pp. 33–43, 2019, doi:10.1016/j.swevo.2019.02.001.; Y. Liu, N. Zhu, K. K. Li, M. Li, J. Zheng, y K. Li, “An angle dominance criterion for evolutionary many-objective optimization”, Inf Sci (N Y), núm. xxxx, 2019, doi:10.1016/j.ins.2018.12.078.; L. Cai, S. Qu, y G. Cheng, “Two-archive method for aggregation-based many-objective optimization”, Inf Sci (N Y), vol. 422, pp. 305–317, 2018, doi:10.1016/j.ins.2017.08.078.; P. J. Fleming, R. C. Purshouse, y R. J. Lygoe, “Many-objective optimization: An engineering design perspective”, Lecture Notes in Computer Science, vol. 3410, pp. 14–32, 2005, doi:10.1007/978-3-540-31880-4_2.; A. Inselberg y B. Dimsdale, “Parallel coordinates: A tool for visualizing multi-dimensional geometry”, pp. 361–378, 1990, doi:10.1007/978-4-431-68057-4_3.; A. Pryke, S. Mostaghim, y A. Nazemi, “Heatmap visualization of population based multi objective algorithms”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4403 LNCS, pp. 361–375, 2007, doi:10.1007/978-3-540-70928-2_29.; D. J. Walker, R. M. Everson, y J. E. Fieldsend, “Visualizing mutually nondominating solution sets in many-objective optimization”, IEEE Transactions on Evolutionary Computation, vol. 17, núm. 2, pp. 165–184, 2013, doi:10.1109/TEVC.2012.2225064.; P. Hoffman y G. Grinstein, “Visualizations for High Dimensional Data Mining-Table Visualizations”, núm. August 2001, 1997.; J. B. Tenenbaum, V. De Silva, y J. C. Langford, “A global geometric framework for nonlinear dimensionality reduction”, Science (1979), vol. 290, núm. 5500, pp. 2319–2323, 2000, doi:10.1126/science.290.5500.2319.; J. Zou, L. Fu, J. Zheng, S. Yang, G. Yu, y Y. Hu, “A many-objective evolutionary algorithm based on rotated grid”, Applied Soft Computing Journal, vol. 67, pp. 596–609, 2018, doi:10.1016/j.asoc.2018.02.031.; B. Khan, S. Hanoun, M. Johnstone, C. P. Lim, D. Creighton, y S. Nahavandi, “A scalarization-based dominance evolutionary algorithm for many-objective optimization”, Inf Sci (N Y), vol. 474, pp. 236–252, 2019, doi:10.1016/j.ins.2018.09.031.; J. Zou, Y. Zhang, S. Yang, Y. Liu, y J. Zheng, “Adaptive neighborhood selection for many-objective optimization problems”, Applied Soft Computing Journal, vol. 64, pp. 186–198, 2018, doi:10.1016/j.asoc.2017.11.041.; M. Wagner y F. Neumann, “A Fast Approximation-Guided Evolutionary Multi-Objective Algorithm”, pp. 687–694.; Q. Zhang, S. Member, y H. Li, “MOEA / D : A Multiobjective Evolutionary Algorithm Based on Decomposition”, vol. 11, núm. 6, pp. 712–731, 2007.; I. Giagkiozis, R. C. Purshouse, y P. J. Fleming, “Generalized Decomposition and Cross Entropy Methods for Many-Objective Optimization”, Inf Sci (N Y), p. 2014, 2014, doi:10.1016/j.ins.2014.05.045.; H. Seada y K. Deb, “U-NSGA-III : A Unified Evolutionary Algorithm for Single , Multiple , and Many-Objective Optimization”, pp. 1–30.; D. Weininger, “SMILES, a Chemical Language and Information System: 1: Introduction to Methodology and Encoding Rules”, J Chem Inf Comput Sci, vol. 28, núm. 1, pp. 31–36, 1988, doi:10.1021/ci00057a005.; L. Hornos, “Introducción a SMILES: Dibujando moléculas en el bloc de notas”, El problema de describir una estructura molecular con caracteres comunes, 2020.; Daylight Chemical Information Systems Inc., “SMILES - A Simplified Chemical Language”, -, 2019.; N. M. O. Boyle, “Towards a Universal SMILES representation - A standard method to generate canonical SMILES based on the InChI”, pp. 1–14, 2012.; IUPAC, “Definición de compuestos anti aromáticos”. https://goldbook.iupac.org/terms/view/A00382; A. T. M. G. Mostafa, J. M. Eakman, M. M. Montoya, y S. L. Yarbro, “Prediction of Heat Capacities of Solid Inorganic Salts from Group Contributions”, pp. 343–348, 1996.; D. W. Van Krevelen, Properties of Polymers: Their Correlation with Chemical Structure; their Numerical Estimation and Prediction from Additive Group Contributions. 2009.; D. Saracino, Abstract Algebra A First Course. 2008.; Joseph A. Gallian, Contemporary Abstract Algebra. 2013.; D. B. Fraleigh, A first course in abstract algebra. 2002.; W. L. Kocay y D. L. Kreher, Graphs, Algorithms, and Optimization, vol. 1. Boca Raton, FL, USA: CRC Press, 2017.; P. Fernández-Gallardo y J. L. Fernández-Pérez, “La Teoría de Pólya”, en El discreto encanto de la matemática, 2002, pp. 1133–1145.; A. R. Matamala, “PÓLYA’S COMBINATORIAL METHOD AND THE ISOMER ENUMERATION PROBLEM”, Bol. Soc. Chil. Quím., 2002. https://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0366-16442002000200006#17; S. Pevac y G. Crundwell, “Pólya’s Isomer Enumeration Method: A Unique Exercise in Group Theory and Combinatorial Analysis for Undergraduates”, J Chem Educ, vol. 77, núm. 10, pp. 1358–1360, 2000, doi:10.1021/ed077p1358.; R. L. Apodaca, “A Comprehensive Treatment of Aromaticity in the SMILES Language”, 2021. https://depth-first.com/articles/2020/02/10/a-comprehensive-treatment-of-aromaticity-in-the-smiles-language/; Daylight Chemical Information Systems Inc., “SMILES Tutorial: Conventions”. https://www.daylight.com/meetings/summerschool98/course/dave/smiles-convent.htm; E. C. Ihmels y J. Gmehling, “Extension and revision of the group contribution method GCVOL for the prediction of pure compound liquid densities”, Ind Eng Chem Res, vol. 42, núm. 2, pp. 408–412, 2003, doi:10.1021/ie020492j.; T. J. Sheldon, C. S. Adjiman, y J. L. Cordiner, “Pure component properties from group contribution: Hydrogen-bond basicity, hydrogen-bond acidity, Hildebrand solubility parameter, macroscopic surface tension, dipole moment, refractive index and dielectric constant”, Fluid Phase Equilib, vol. 231, núm. 1, pp. 27–37, 2005, doi:10.1016/j.fluid.2004.12.017.; J. Gmehling, J. Lohmann, A. Jakob, J. Li, y R. Joh, “A modified UNIFAC (Dortmund) model. 4. Revision and extension”, Ind Eng Chem Res, vol. 37, núm. 12, pp. 4876–4882, 1998, doi:10.1021/ie980347z.; D. Constantinescu y J. Gmehling, “Further development of modified UNIFAC (Dortmund): Revision and extension 6”, J Chem Eng Data, vol. 61, núm. 8, pp. 2738–2748, 2016, doi:10.1021/acs.jced.6b00136.; A. Fredeslund, Russell L. Jones, y J. M. Prausnitz, “Group-Contri bution Estimation of Activity Coefficients in Nonideal Liquid Mixtures”, vol. 21, núm. 6, 1975.; Dortmund-Databank, “Published ParametersUnifac”. http://www.ddbst.com/published-parameters-unifac.html; J. J. Irwin y B. K. Shoichet, “ZINC - A free database of commercially available compounds for virtual screening”, J Chem Inf Model, vol. 45, núm. 1, pp. 177–182, 2005, doi:10.1021/ci049714+.; T. Sterling y J. J. Irwin, “ZINC 15 - Ligand Discovery for Everyone”, J Chem Inf Model, vol. 55, núm. 11, pp. 2324–2337, 2015, doi:10.1021/acs.jcim.5b00559.; EPA, “Distributed Structure-Searchable Toxicity (DSSTox) Database”, 2022. https://www.epa.gov/chemical-research/distributed-structure-searchable-toxicity-dsstox-database.; Texas A&M University Libraries, “Chemical Pricing Database - Beta Version”, 2022. https://tamu.libguides.com/c.php?g=587308&p=5694124&url=L2V2LTI5ODk3NjIvZGIvNTUzNTQvdmlldy5hc3B4; S&P Global, “Chemical Week by S&P Global”, 2022. https://chemweek.com/home; Relx Inc., “ICIS Chemical Bussiness”, 2022. https://www.icis.com/subscriber/specialpublications/#_=_; T. Group, G. All, I. N. D. Farm, P. F. F. Farm, y G. Smith, “Table 9 . Producer price indexes for commodity and service groupings and individual items , not seasonally adjusted [April 2022, Index base 1982=100, unless otherwise indicated]”, núm. April, pp. 1–58, 2022.; S. Müller, “GitHub - Simon Müller - Fragmentation Algorithm Paper”, https://github.com/simonmb/fragmentation_algorithm_paper, 2023. https://github.com/simonmb/fragmentation_algorithm_paper (consultado el 8 de abril de 2023).; S. Müller, “Flexible heuristic algorithm for automatic molecule fragmentation: Application to the UNIFAC group contribution model”, J Cheminform, vol. 11, núm. 1, 2019, doi:10.1186/s13321-019-0382-3.; J. Prieto y J. Gomez, “Hybrid Adaptive Evolutionary Algorithm for Multi-objective Optimization”, 2020, [En línea]. Disponible en: http://arxiv.org/abs/2004.13925; DDBST GmbH, “Parameters of the Modified UNIFAC (Dortmund) Model”, 2022. http://unifac.ddbst.de/PublishedParametersUNIFACDO.html; OEIS.org, “The On-Line Encyclopedia of Integer Sequences® (OEIS®)”, https://oeis.org/, 2023. https://oeis.org/ (consultado el 25 de julio de 2023).; N. C. for B. I. NIH - National Library of Medicine, “PubChem: Explore Chemistry - Quickly find chemical information from authoritative sources”, 2023.; J. Gómez, “Hybrid Adaptive Evolutionary Algorithm Hyper Heuristic”, pp. 1–5.; Inc. Daylight Chemical Information System, “SMARTS - A Language for Describing Molecular Patterns”, https://www.daylight.com/dayhtml/doc/theory/theory.smarts.html, 2019. https://www.daylight.com/dayhtml/doc/theory/theory.smarts.html (consultado el 8 de abril de 2023).; U. H. ZBH - Center for Bioinformatics, “SMARTS PLUS”, https://smarts.plus/, 2023. https://smarts.plus/ (consultado el 8 de abril de 2023).; U. Weidlich y J. Gmehling, “A Modified UNIFAC Model. 1. Prediction of VLE, hE, and 3∞”, Ind Eng Chem Res, vol. 26, núm. 7, pp. 1372–1381, 1987, doi:10.1021/ie00067a018.; A. Ag, Ε. Fredenslund, J. Gmehling, y P. Rasmussen, Vapor-liquid equilib using UNIFAC a group-contribution method Library ol Congress Cataloging in Publication Data. 1977.; ACD Labs, “ACD Labs ChemSketch”, https://www.acdlabs.com/resources/free-chemistry-software-apps/chemsketch-freeware/, 2023. https://www.acdlabs.com/resources/free-chemistry-software-apps/chemsketch-freeware/ (consultado el 13 de julio de 2022).; https://repositorio.unal.edu.co/handle/unal/85692; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/
-
11
Autoři: a další
Zdroj: Nutrients. Jan2024, Vol. 16 Issue 1, p61. 21p.
Plný text ve formátu HTML Plný text ve formátu PDF -
12
Autoři: a další
Zdroj: Hydrology & Earth System Sciences Discussions; 7/12/2024, p1-40, 40p
Témata: SNOW cover, SNOWMELT, ALPINE glaciers, GEOLOGICAL surveys, HYDROLOGIC models, GLACIERS, MASS budget (Geophysics)
Korporace: GEOLOGICAL Survey (U.S.)
-
13
Autoři: Yang, David Y.
Zdroj: Journal of Infrastructure Systems; Sep2022, Vol. 28 Issue 3, p1-14, 14p
-
14
Autoři: a další
Zdroj: Land (2012); Nov2024, Vol. 13 Issue 11, p1932, 23p
-
15
Autoři: a další
Zdroj: Water Resources Research; Mar2023, Vol. 59 Issue 3, p1-18, 18p
Témata: WATER quality, AGRICULTURE, SPRINKLER irrigation, ATTENUATION coefficients, DATA structures, WATER quality monitoring, MICROIRRIGATION
Geografický termín: IDAHO
-
16
Autoři: a další
Zdroj: SPE Polymers; Oct2025, Vol. 6 Issue 4, p1-24, 24p
-
17
Autoři:
Zdroj: Pertanika Journal of Science & Technology; Oct2025, Vol. 33 Issue 6, p2641-2663, 23p
-
18
Autoři: a další
Zdroj: Expert Systems; May2022, Vol. 39 Issue 4, p1-21, 21p
-
19
Autoři: a další
Zdroj: Multiscale & Multidisciplinary Modeling, Experiments & Design; Jul2025, Vol. 8 Issue 7, p1-47, 47p
-
20
Autoři:
Zdroj: Sensors (14248220); Feb2025, Vol. 25 Issue 3, p955, 30p
Full Text Finder
Nájsť tento článok vo Web of Science