Prediction of silicon content in hot metal based on golden sine particle swarm optimization and random forest

Particle Swarm Optimization (PSO) algorithm quickly falls into local optimum, low precision. In this paper, add the golden sine operation to the particle position update. The results show that the improved PSO algorithm has better optimization ability. The main parameters affecting the silicon conte...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ch. Hu, K. Yang
Formato:	article
Lenguaje:	EN
Publicado:	Croatian Metallurgical Society 2022
Materias:	blast furnace hot metal silicon particle swarm optimization golden sine algorithm random forest Mining engineering. Metallurgy TN1-997
Acceso en línea:	https://doaj.org/article/bc29ebf22c32404fa58e4f8ceb2e9fab
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:bc29ebf22c32404fa58e4f8ceb2e9fab
record_format	dspace
spelling	oai:doaj.org-article:bc29ebf22c32404fa58e4f8ceb2e9fab2021-11-28T20:04:11ZPrediction of silicon content in hot metal based on golden sine particle swarm optimization and random forest0543-58461334-2576https://doaj.org/article/bc29ebf22c32404fa58e4f8ceb2e9fab2022-01-01T00:00:00Zhttps://hrcak.srce.hr/file/386154https://doaj.org/toc/0543-5846https://doaj.org/toc/1334-2576Particle Swarm Optimization (PSO) algorithm quickly falls into local optimum, low precision. In this paper, add the golden sine operation to the particle position update. The results show that the improved PSO algorithm has better optimization ability. The main parameters affecting the silicon content in hot metal are selected. Then, calculate the correlation coefficient and significance level between parameters and silicon content in hot metal. Finally, the prediction model of silicon content in hot metal is established based on the Random Forest (RF) optimized by improved PSO. The results show that the hit rate is 87,17 %.Ch. HuK. YangCroatian Metallurgical Societyarticleblast furnacehot metalsiliconparticle swarm optimizationgolden sine algorithmrandom forestMining engineering. MetallurgyTN1-997ENMetalurgija, Vol 61, Iss 2, Pp 325-328 (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	blast furnace hot metal silicon particle swarm optimization golden sine algorithm random forest Mining engineering. Metallurgy TN1-997
spellingShingle	blast furnace hot metal silicon particle swarm optimization golden sine algorithm random forest Mining engineering. Metallurgy TN1-997 Ch. Hu K. Yang Prediction of silicon content in hot metal based on golden sine particle swarm optimization and random forest
description	Particle Swarm Optimization (PSO) algorithm quickly falls into local optimum, low precision. In this paper, add the golden sine operation to the particle position update. The results show that the improved PSO algorithm has better optimization ability. The main parameters affecting the silicon content in hot metal are selected. Then, calculate the correlation coefficient and significance level between parameters and silicon content in hot metal. Finally, the prediction model of silicon content in hot metal is established based on the Random Forest (RF) optimized by improved PSO. The results show that the hit rate is 87,17 %.
format	article
author	Ch. Hu K. Yang
author_facet	Ch. Hu K. Yang
author_sort	Ch. Hu
title	Prediction of silicon content in hot metal based on golden sine particle swarm optimization and random forest
title_short	Prediction of silicon content in hot metal based on golden sine particle swarm optimization and random forest
title_full	Prediction of silicon content in hot metal based on golden sine particle swarm optimization and random forest
title_fullStr	Prediction of silicon content in hot metal based on golden sine particle swarm optimization and random forest
title_full_unstemmed	Prediction of silicon content in hot metal based on golden sine particle swarm optimization and random forest
title_sort	prediction of silicon content in hot metal based on golden sine particle swarm optimization and random forest
publisher	Croatian Metallurgical Society
publishDate	2022
url	https://doaj.org/article/bc29ebf22c32404fa58e4f8ceb2e9fab
work_keys_str_mv	AT chhu predictionofsiliconcontentinhotmetalbasedongoldensineparticleswarmoptimizationandrandomforest AT kyang predictionofsiliconcontentinhotmetalbasedongoldensineparticleswarmoptimizationandrandomforest
_version_	1718407794424545280

Prediction of silicon content in hot metal based on golden sine particle swarm optimization and random forest

Ejemplares similares