A Cyclical Non-Linear Inertia-Weighted Teaching–Learning-Based Optimization Algorithm

Wu, Ziyan; Ren, Xue

doi:10.3390/a12050094

Cited by 4 publications

(8 citation statements)

References 37 publications

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“…In addition, the R 2 in Table 7 means the goodness of fit. e closer the value of R 2 to 1, the better the fit of the regression line to the observations [42].…”

Section: Resultsmentioning

confidence: 97%

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

Pan

2019

Mathematical Problems in Engineering

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Forecasting energy data, especially the primary energy requirement, is the key part of policy-making. For those territories of different developing types, seeking a knowledge-based and dependable forecasting model is an essential prerequisite for the prosperous development of policy-making. In this paper, both autoregressive integrated moving average and backpropagation neural network models which have been proved to be very efficient in forecasting are applied to the forecasts of the primary energy consumption of three different developing types of territories. It is shown that the average relative errors between the actual data and simulated value are from 4.5% to 5.9% by the autoregressive integrated moving average and from 0.04% to 0.47% by the backpropagation neural network. Specially, this research shows that the backpropagation neural network model presents a better prediction of primary energy requirement when considering gross domestic product, population, and the particular values as predictors. Furthermore, we indicate that the single-input backpropagation neural network model can still work when the particular values have contributed most to the energy consumption.

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“…In addition, the R 2 in Table 7 means the goodness of fit. e closer the value of R 2 to 1, the better the fit of the regression line to the observations [42].…”

Section: Resultsmentioning

confidence: 97%

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

Pan

2019

Mathematical Problems in Engineering

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“…Meanwhile, fuzzy TLBO required the most time to classify all datasets. This could be because the fuzzy TLBO's slow training procedure utilized a large amount of computer memory, making it time demanding [193]- [195]. The comparison of time expresses in Table 2.…”

Section: Resultsmentioning

confidence: 99%

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Aseri

Ismail

Fakharudin

et al. 2022

IJ-AI

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The world health organization (WHO) proclaimed the COVID-19, commonly known as the coronavirus disease 2019, was a pandemic in March 2020. When people are in close proximity to one another, the virus spreads mostly through the air. It causes some symptoms in the affected person. COVID-19 symptoms are quite variable, ranging from none to severe sickness. As a result, the fuzzy method is seen favourably as a tool for determining the severity of a person’s COVID-19 sickness. However, when applied to a large situation, manually generating a fuzzy parameter is challenging. This could be because of the identification of a large number of fuzzy parameters. A mechanism, such as an automatic procedure, is consequently required to identify the right fuzzy parameters. The metaheuristic algorithm is regarded as a viable strategy. Five meta-heuristic algorithms were analyzed and utilized in this article to classify the severity of COVID-19 sickness data. The performance of the five meta-heuristic algorithms was evaluated using the COVID-19 symptoms dataset. The COVID-19 symptom dataset was created in accordance with WHO and the Indian ministry of health and family welfare criteria. The findings provide the average classification accuracy for each approach.

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“…Muitos algoritmos de otimização clássicos inspirados na natureza, baseados na utilização de população, têm sido propostos para tentar resolver problemas de otimização, para os quais soluções robustas são difíceis ou impossíveis de encontrar em tempo polinomial usando abordagens tradicionais [6].…”

Section: -Fundamentação Teóricaunclassified

“…Onde i representa cada variável do problema, variando entre 1 e n. Os valores dos feromônios das melhores soluções são depositados na matriz de feromônios τ, segundo (6):…”

Section: -Discretização Das Variáveisunclassified

“…Reforçar o feromônio em todos os pontos da melhor solução encontrada nesta iteração, utilizando(6), de forma que a matriz do feromônio sempre tenha quantidade maior de feromônio nos caminhos que são mais atrativos.Etapa 11:Atualizar o valor de fmin para o valor da melhor solução encontrada na atual iteração nc, caso esse valor seja menor que fmin. Neste momento, fmin deve armazenar o valor heurístico da melhor solução encontrada na iteração atual.…”

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Aplicação de parâmetros autoadaptativos de ajuste de domínio na otimização de funções contínuas utilizando colônia de formigas

Freitas¹

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A Cyclical Non-Linear Inertia-Weighted Teaching–Learning-Based Optimization Algorithm

Cited by 4 publications

References 37 publications

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Aplicação de parâmetros autoadaptativos de ajuste de domínio na otimização de funções contínuas utilizando colônia de formigas

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