Teaching–Learning Based Optimization (TLBO) with Variable Neighborhood Search to Retail Shelf-Space Allocation

Chen, Yan‐Kwang; Weng, Shi-Xin; Liu, Tsai-Pei

doi:10.3390/math8081296

Cited by 12 publications

(7 citation statements)

References 28 publications

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“…We used TLBO (19) to lower the number of features. This algorithm comprises two phases: 1) the Teacher phase, in which the learners get the knowledge from the teacher as the best solution available, and 2) the Learner phase, in which learners learn through interacting with each other (20, 21). This method is based on a teacher’s influence on students’ output in a class, which means it works based on the man.…”

Section: Methodsmentioning

confidence: 99%

An Integrated Neural Network and Evolutionary Algorithm Approach for Liver Fibrosis Staging: Can Artificial Intelligence Reduce Patient Costs?

Nazarizadeh,

Banirostam,

Biglari

et al. 2024

Preprint

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Background: Liver fibrosis is important in terms of staging, and liver biopsy is the gold standard diagnostic tool. We aim to design and evaluate an artificial neural network (ANN) method by taking advantage of the Teaching Learning Based Optimization (TLBO) algorithm for the prediction of liver fibrosis stage in blood donors and hepatitis C. Method: We proposed a method based on a selection of machine learning classification methods including Multi Layers Perceptron neural network (MLP), Naive Bayesian (NB), decision tree, and deep learning. Initially, the Synthetic minority oversampling technique (SMOTE) was performed to address the imbalance of the dataset. Afterward, the integration of MLP and TLBO was implemented. Result: We proposed a novel algorithm that reduced the number of required patient features to 7 inputs. The accuracy of MLP using 12 features is 0.903, while the accuracy of the proposed MLP with the TLBO method is 0.891. Besides, the diagnostic accuracy in all methods, except the model designed with the Bayesian Network, increased when the SMOTE balancer was applied. Conclusion: The Decision tree deep learning methods showed the highest levels of accuracy with 12 features. Interestingly, with the use of TLBO and 7 features, the MLP reached a 0.891 accuracy rate which is quite satisfying compared with similar studies. The proposed model provided great diagnostic accuracy by reducing the required properties from the samples without reducing the accuracy. The results of our study showed that the recruited algorithm of our study was more straightforward, with lower required properties and similar accuracy.

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Section: Methodsmentioning

confidence: 99%

An Integrated Neural Network and Evolutionary Algorithm Approach for Liver Fibrosis Staging: Can Artificial Intelligence Reduce Patient Costs?

Nazarizadeh,

Banirostam,

Biglari

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…We used TLBO 15 to reduce the number of features. This algorithm comprises two phases: (i) the Teacher phase, in which the learners get the knowledge from the teacher as the best solution available, and (ii) the Learner phase, in which learners learn through interacting with each other 19,20 . This method is based on a teacher's influence on students' output in a class, which means it works based on the mean.…”

Section: Methodsmentioning

confidence: 99%

“…This algorithm comprises two phases: (i) the Teacher phase, in which the learners get the knowledge from the teacher as the best solution available, and (ii) the Learner phase, in which learners learn through interacting with each other. 19,20 This method is based on a teacher's influence on students' output in a class, which means it works based on the mean. The teacher is the best way of selecting, and students' output is defined according to the mean of each selection method.…”

Section: Study Outcomementioning

confidence: 99%

Integrated neural network and evolutionary algorithm approach for liver fibrosis staging: Can artificial intelligence reduce patient costs?

Nazarizadeh,

Banirostam,

Biglari

et al. 2024

JGH Open

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Background and AimStaging liver fibrosis is important, and liver biopsy is the gold standard diagnostic tool. We aim to design and evaluate an artificial neural network (ANN) method by taking advantage of the Teaching Learning‐Based Optimization (TLBO) algorithm for the prediction of liver fibrosis stage in blood donors and hepatitis C patients.MethodsWe propose a method based on a selection of machine learning classification methods including multilayer perceptron (MLP) neural network, Naive Bayesian (NB), decision tree, and deep learning. Initially, the synthetic minority oversampling technique (SMOTE) is performed to address the imbalance in the dataset. Afterward, the integration of MLP and TLBO is implemented.ResultsWe propose a novel algorithm that reduces the number of required patient features to seven inputs. The accuracy of MLP using 12 features is 0.903, while that of the proposed MLP with TLBO is 0.891. Besides, the diagnostic accuracy of all methods, except the model designed with the Bayesian network, increases when the SMOTE balancer is applied.ConclusionThe decision tree‐based deep learning methods show the highest levels of accuracy with 12 features. Interestingly, with the use of TLBO and seven features, MLP reached an accuracy rate of 0.891, which is quite satisfactory when compared with those of similar studies. The proposed model provides high diagnostic accuracy, while reducing the required number of properties from the samples. The results of our study show that the recruited algorithm of our study is more straightforward, with a smaller number of required properties and similar accuracy.

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“…(v) Multi-objective genetic algorithm: TLBO [36][37][38]; and (vi) Pareto-optimal forward-facing: illustration of resolutions in function space. Therefore, to examine the ideal execution of the centrifugal pump, the pump models are used in the three-objective streamlining optimum [39][40][41][42]. The three-objective optimum problem is well-defined and explained in the following Equation (21).…”

Section: Process Of the Optimum Designmentioning

confidence: 99%

CFD Analysis and Optimum Design for a Centrifugal Pump Using an Effectively Artificial Intelligent Algorithm

et al. 2022

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In this study, we proposed a novel approach to improve centrifugal pump performance with regard to the pump head, pump efficiency, and power. Firstly, to establish constraints, an optimal numerical model that accounted for factors such as pump efficiency and the head was considered. The pump was designed, and an artificial intelligence algorithmic approach was applied to the pump before performing experiments. We considered a set of models by selecting the parameters of the centrifugal pump casing section area, the interference of the impeller, the volute tongue length, and the volute tongue angle. The weights of the factors of safety and displacement on the optimization indices were estimated. The matrix of the weights for the optimal process was less than 38% or greater than 62%. This approach guarantees a complicated multi-objective optimization problem. The results show that the centrifugal pump performances were improved.

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Teaching–Learning Based Optimization (TLBO) with Variable Neighborhood Search to Retail Shelf-Space Allocation

Cited by 12 publications

References 28 publications

An Integrated Neural Network and Evolutionary Algorithm Approach for Liver Fibrosis Staging: Can Artificial Intelligence Reduce Patient Costs?

An Integrated Neural Network and Evolutionary Algorithm Approach for Liver Fibrosis Staging: Can Artificial Intelligence Reduce Patient Costs?

Integrated neural network and evolutionary algorithm approach for liver fibrosis staging: Can artificial intelligence reduce patient costs?

CFD Analysis and Optimum Design for a Centrifugal Pump Using an Effectively Artificial Intelligent Algorithm

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