Application of the simulated annealing algorithm to the correlated WMP radiation model for flames

Silva, B. H.; Machado, Isaias; Pereira, Fernando M.; Pagot, Paulo Roberto; França, Francis

doi:10.1080/17415977.2020.1732956

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…(Huang et al, 2012). The third search result was Silva et al (2020), which discusses the determination of optimal parameters to model the radiation profile emitted by flames using the weightedmulti-point-source model, whatever that means. What is clear is that the parameters of the models in these three studies are not related in any way to the positions of atoms, and their use of the metaphors of temperature and potential are truly abstract.…”

Section: Hate #1 Molecular-dynamics Refinement Is Taken Too Literallymentioning

confidence: 99%

Ten things I `hate' about refinement

Roversi

Tronrud

2021

Acta Crystallogr D Struct Biol

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Macromolecular refinement is an optimization process that aims to produce the most likely macromolecular structural model in the light of experimental data. As such, macromolecular refinement is one of the most complex optimization problems in wide use. Macromolecular refinement programs have to deal with the complex relationship between the parameters of the atomic model and the experimental data, as well as a large number of types of prior knowledge about chemical structure. This paper draws attention to areas of unfinished business in the field of macromolecular refinement. In it, we describe ten refinement topics that we think deserve attention and discuss directions leading to macromolecular refinement software that would make the best use of modern computer resources to meet the needs of structural biologists of the twenty-first century.

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Section: Hate #1 Molecular-dynamics Refinement Is Taken Too Literallymentioning

confidence: 99%

Ten things I `hate' about refinement

Roversi

Tronrud

2021

Acta Crystallogr D Struct Biol

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“…The SA algorithm is a physical annealing method based on simulation that has successfully extended to multiple dynamic problem optimization. The concept behind the SA stems from thermodynamics and illustrates how to mirror the solidifying mechanism of the fluid into a crystalline solid(Abdel-Basset et al 2021;Redi et al 2020;Silva et al 2020;Tang et al 2020).…”

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confidence: 99%

Application of novel binary optimized machine learning models for monthly streamflow prediction

et al. 2023

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Accurate measurements of available water resources play a key role in achieving a sustainable environment of a society. Precise river flow estimation is an essential task for optimal use of hydropower generation, flood forecasting, and best utilization of water resources in river engineering. The current paper presents the development and verification of the prediction abilities of new hybrid extreme learning machine (ELM)-based models coupling with metaheuristic methods, e.g., Particle swarm optimization (PSO), Mayfly optimization algorithm (MOA), Grey wolf optimization (GWO), and simulated annealing (SA) for monthly streamflow prediction. Prediction precision of standalone ELM model was compared with two-phase optimized state-of-the-arts models, e.g., ELM–PSO, ELM–MOA, ELM–PSOGWO, and ELM–SAMOA, respectively. Hydro-meteorological data acquired from Gorai and Padma Hardinge Bridge stations at Padma River Basin, northwestern Bangladesh, were utilized as inputs in this study to employ models in the form of seven different input combinations. The model’s performances are appraised using Nash–Sutcliffe efficiency, root-mean-square-error (RMSE), mean absolute error, mean absolute percentage error and determination coefficient. The tested results of both stations reported that the ELM–SAMOA and ELM–PSOGWO models offered the best accuracy in the prediction of monthly streamflows compared to ELM–PSO, ELM–MOA, and ELM models. Based on the local data, the ELM–SAMOA reduced the RMSE of ELM, ELM–PSO, ELM–MOA, and ELM–PSOGWO by 31%, 27%, 19%, and 14% for the Gorai station and by 29%, 27%, 19%, and 14% for Padma Hardinge bridge station, in the testing stage, respectively. In contrast, based on external data, ELM–PSOGWO improves in RMSE of ELM, ELM–PSO, ELM–MOA, and ELM–SAMOA by 20%, 5.1%, 6.2%, and 4.6% in the testing stage, respectively. The results confirmed the superiority of two-phase optimized ELM–SAMOA and ELM–PSOGWO models over a single ELM model. The overall results suggest that ELM–SAMOA and ELM–PSOGWO models can be successfully applied in modeling monthly streamflow prediction with either local or external hydro-meteorological datasets.

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