How to cite this paper
Chávez, D., Muñoz, S., Coral, D & Lozada, C. (2024). Performance evaluation of the NGHS metaheuristic as an alternative to the dynamic adaptive GA in the CREASE tool in SAS profile analysis of nanoparticulate systems.International Journal of Industrial Engineering Computations , 15(4), 833-844.
Refrences
Beltran-Villegas, D. J., Wessels, M. G., Lee, J. Y., Song, Y., Wooley, K. L., Pochan, D. J., & Jayaraman, A. (2019). Computational Reverse-Engineering Analysis for Scattering Experiments on Amphiphilic Block Polymer Solutions. Journal of the American Chemical Society, 141(37), 14916–14930. https://doi.org/10.1021/jacs.9b08028
Breßler, I., Kohlbrecher, J., & Thünemann, A. F. (2015). SASfit: A tool for small-angle scattering data analysis using a library of analytical expressions. Journal of Applied Crystallography, 48(5), 1587–1598. https://doi.org/10.1107/s1600576715016544
Coral-Coral, D. F., & Mera-Córdoba, J. A. (2019). Applying SAXS to study the structuring of Fe3O4 magnetic nanoparticles in colloidal suspensions. DYNA, 86(209), 135–140. https://doi.org/10.15446/dyna.v86n209.73450
Dubey, M., Kumar, V., Kaur, M., & Dao, T. P. (2021). A Systematic Review on Harmony Search Algorithm: Theory, Literature, and Applications. Mathematical Problems in Engineering, 2021(1), 5594267. https://doi.org/10.1155/2021/5594267
Ghiduk, A. S., & Alharbi, A. (2022). Generating of Test Data by Harmony Search Against Genetic Algorithms. Intelligent Automation & Soft Computing, 36(1), 647–665. https://doi.org/10.32604/IASC.2023.031865
Glatter, O., & Kratky, O. (Eds.). (1982). Small angle X-ray scattering. Academic Press.
Heil, C. M., Patil, A., Dhinojwala, A., & Jayaraman, A. (2022). Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) with Machine Learning Enhancement to Determine Structure of Nanoparticle Mixtures and Solutions. ACS Central Science, 8(7), 996–1007. https://doi.org/10.1021/acscentsci.2c00382
Jeffries, C. M., Ilavsky, J., Martel, A., Hinrichs, S., Meyer, A., Pedersen, J. S., Sokolova, A. V., & Svergun, D. I. (2021). Small-angle X-ray and neutron scattering. Nature Reviews Methods Primers, 1(1), 1–39. https://doi.org/10.1038/s43586-021-00064-9
Omran, M. G. H., & Mahdavi, M. (2008). Global-best harmony search. Applied Mathematics and Computation, 198(2), 643–656. https://doi.org/10.1016/j.amc.2007.09.004
Pan, Q. K., Suganthan, P. N., Tasgetiren, M. F., & Liang, J. J. (2010). A self-adaptive global best harmony search algorithm for continuous optimization problems. Applied Mathematics and Computation, 216(3), 830–848. https://doi.org/10.1016/J.AMC.2010.01.088
Peraza, C., Valdez, F., & Castillo, O. (2014). A harmony search algorithm comparison with genetic algorithms. In O. Castillo & P. Melin (Eds.), Studies in Computational Intelligence (Vol. 574, pp. 105–123). Springer Verlag. https://doi.org/10.1007/978-3-319-10960-2_7
Petoukhov, M. V., & Svergun, D. I. (2005). Global rigid body modeling of macromolecular complexes against small-angle scattering data. Biophysical Journal, 89(2), 1237–1250. https://doi.org/10.1529/biophysj.105.064154
Qin, F., Zain, A. M., & Zhou, K. Q. (2022). Harmony search algorithm and related variants: A systematic review. Swarm and Evolutionary Computation, 74, 101–126. https://doi.org/10.1016/j.swevo.2022.101126
Ranjbar, N., Anvari, S., & Delavar, M. (2021). The application of harmony search and genetic algorithms for the simultaneous optimization of integrated reservoir–FARM systems (IRFS)*. Irrigation and Drainage, 70(4), 743–756. https://doi.org/10.1002/IRD.2567
Ruano-Daza, E., Cobos, C., Torres-Jimenez, J., Mendoza, M., & Paz, A. (2018). A multiobjective bilevel approach based on global-best harmony search for defining optimal routes and frequencies for bus rapid transit systems. Applied Soft Computing, 67, 567–583. https://doi.org/10.1016/J.ASOC.2018.03.026
Schnablegger, H., & Singh, Y. (2023). The SAXS Guide Getting acquainted with the principles (5th ed.). Anton Paar GmbH. www.anton-paar.com
Vasconcelos, J. A., Ramírez, J. A., Takahashi, R. H. C., & Saldanha, R. R. (2001). Improvements in genetic algorithms. IEEE Transactions on Magnetics, 37(5 I), 3414–3417. https://doi.org/10.1109/20.952626
Wessels, M. G., & Jayaraman, A. (2021). Machine Learning Enhanced Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) to Determine Structures in Amphiphilic Polymer Solutions. ACS Polymers Au, 1(3), 153–164. https://doi.org/10.1021/ACSPOLYMERSAU.1C00015
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82. https://doi.org/10.1109/4235.585893
Ye, Z., Wu, Z., & Jayaraman, A. (2021). Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) on Vesicles Assembled from Amphiphilic Macromolecular Solutions. JACS Au, 1(11), 1925–1936. https://doi.org/10.1021/jacsau.1c00305
Zou, D., Gao, L., Wu, J., Li, S., & Li, Y. (2010). A novel global harmony search algorithm for reliability problems. Computers & Industrial Engineering, 58(2), 307–316. https://doi.org/10.1016/J.CIE.2009.11.003
Breßler, I., Kohlbrecher, J., & Thünemann, A. F. (2015). SASfit: A tool for small-angle scattering data analysis using a library of analytical expressions. Journal of Applied Crystallography, 48(5), 1587–1598. https://doi.org/10.1107/s1600576715016544
Coral-Coral, D. F., & Mera-Córdoba, J. A. (2019). Applying SAXS to study the structuring of Fe3O4 magnetic nanoparticles in colloidal suspensions. DYNA, 86(209), 135–140. https://doi.org/10.15446/dyna.v86n209.73450
Dubey, M., Kumar, V., Kaur, M., & Dao, T. P. (2021). A Systematic Review on Harmony Search Algorithm: Theory, Literature, and Applications. Mathematical Problems in Engineering, 2021(1), 5594267. https://doi.org/10.1155/2021/5594267
Ghiduk, A. S., & Alharbi, A. (2022). Generating of Test Data by Harmony Search Against Genetic Algorithms. Intelligent Automation & Soft Computing, 36(1), 647–665. https://doi.org/10.32604/IASC.2023.031865
Glatter, O., & Kratky, O. (Eds.). (1982). Small angle X-ray scattering. Academic Press.
Heil, C. M., Patil, A., Dhinojwala, A., & Jayaraman, A. (2022). Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) with Machine Learning Enhancement to Determine Structure of Nanoparticle Mixtures and Solutions. ACS Central Science, 8(7), 996–1007. https://doi.org/10.1021/acscentsci.2c00382
Jeffries, C. M., Ilavsky, J., Martel, A., Hinrichs, S., Meyer, A., Pedersen, J. S., Sokolova, A. V., & Svergun, D. I. (2021). Small-angle X-ray and neutron scattering. Nature Reviews Methods Primers, 1(1), 1–39. https://doi.org/10.1038/s43586-021-00064-9
Omran, M. G. H., & Mahdavi, M. (2008). Global-best harmony search. Applied Mathematics and Computation, 198(2), 643–656. https://doi.org/10.1016/j.amc.2007.09.004
Pan, Q. K., Suganthan, P. N., Tasgetiren, M. F., & Liang, J. J. (2010). A self-adaptive global best harmony search algorithm for continuous optimization problems. Applied Mathematics and Computation, 216(3), 830–848. https://doi.org/10.1016/J.AMC.2010.01.088
Peraza, C., Valdez, F., & Castillo, O. (2014). A harmony search algorithm comparison with genetic algorithms. In O. Castillo & P. Melin (Eds.), Studies in Computational Intelligence (Vol. 574, pp. 105–123). Springer Verlag. https://doi.org/10.1007/978-3-319-10960-2_7
Petoukhov, M. V., & Svergun, D. I. (2005). Global rigid body modeling of macromolecular complexes against small-angle scattering data. Biophysical Journal, 89(2), 1237–1250. https://doi.org/10.1529/biophysj.105.064154
Qin, F., Zain, A. M., & Zhou, K. Q. (2022). Harmony search algorithm and related variants: A systematic review. Swarm and Evolutionary Computation, 74, 101–126. https://doi.org/10.1016/j.swevo.2022.101126
Ranjbar, N., Anvari, S., & Delavar, M. (2021). The application of harmony search and genetic algorithms for the simultaneous optimization of integrated reservoir–FARM systems (IRFS)*. Irrigation and Drainage, 70(4), 743–756. https://doi.org/10.1002/IRD.2567
Ruano-Daza, E., Cobos, C., Torres-Jimenez, J., Mendoza, M., & Paz, A. (2018). A multiobjective bilevel approach based on global-best harmony search for defining optimal routes and frequencies for bus rapid transit systems. Applied Soft Computing, 67, 567–583. https://doi.org/10.1016/J.ASOC.2018.03.026
Schnablegger, H., & Singh, Y. (2023). The SAXS Guide Getting acquainted with the principles (5th ed.). Anton Paar GmbH. www.anton-paar.com
Vasconcelos, J. A., Ramírez, J. A., Takahashi, R. H. C., & Saldanha, R. R. (2001). Improvements in genetic algorithms. IEEE Transactions on Magnetics, 37(5 I), 3414–3417. https://doi.org/10.1109/20.952626
Wessels, M. G., & Jayaraman, A. (2021). Machine Learning Enhanced Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) to Determine Structures in Amphiphilic Polymer Solutions. ACS Polymers Au, 1(3), 153–164. https://doi.org/10.1021/ACSPOLYMERSAU.1C00015
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82. https://doi.org/10.1109/4235.585893
Ye, Z., Wu, Z., & Jayaraman, A. (2021). Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) on Vesicles Assembled from Amphiphilic Macromolecular Solutions. JACS Au, 1(11), 1925–1936. https://doi.org/10.1021/jacsau.1c00305
Zou, D., Gao, L., Wu, J., Li, S., & Li, Y. (2010). A novel global harmony search algorithm for reliability problems. Computers & Industrial Engineering, 58(2), 307–316. https://doi.org/10.1016/J.CIE.2009.11.003