K-means cluster interactive algorithm-based evolutionary approach for solving bilevel multi-objective programming problems

Abo-Elnaga, Yousria; Nasr, S. M.

doi:10.1016/j.aej.2021.04.098

Cited by 27 publications

(18 citation statements)

References 26 publications

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“…K-means cluster is a method used in grouping data [32], [33]. Basically, the cluster concept is one of the techniques in which there is unsupervised learning in data mining by grouping data features that have similar features [34].…”

Section: K-means Clustermentioning

confidence: 99%

Deep learning approach analysis model prediction and classification poverty status

Yanto

Wiyandra²,

Defit³

2023

IJ-AI

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The problem of poverty is a scourge for every developing country coupled with the economic crisis that occurred during the COVID-19 pandemic. The impact of these problems is felt directly by the people in Indonesia, especially in the Province of West Sumatra. This study aims to predict and classify the level of poverty status by developing an analytical model based on the deep learning (DL) approach. The methods used in this study include the K-Means method, artificial neural network (ANN), and support vector Machine (SVM). The analytical model will be optimized using the Pearson Correlation (PC) method to measure the accuracy of the analysis. The variable indicator uses the parameters of population (X1), poverty rate (X2), income (X3), and poverty percentage (X4). The results of the study present prediction and classification output with a validity level of accuracy of 99.8%. Based on these results, it can be concluded that the proposed DL analysis model can present an updated analytical model that is quite effective in carrying out the prediction and classification process. The research findings also contribute to the initial handling of the problem of poverty.

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Section: K-means Clustermentioning

confidence: 99%

Deep learning approach analysis model prediction and classification poverty status

Yanto

Wiyandra²,

Defit³

2023

IJ-AI

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“…So far, MP has been well applied to many real-life scenarios, such as missile defense (Haywood et al 2022 ). Furthermore, since MP is NP-hard (Ben-Ayed and Blair 1990 ), scholars have introduced many algorithms, such as evolutionary algorithm (Abo-Elnaga and Nasr 2022 ) and branch-and-bound algorithm (Liu et al. 2021 ).…”

Section: Related Workmentioning

confidence: 99%

Uncertain multilevel programming with application to omni-channel vehicle routing problem

Gao

Ralescu

2022

J Ambient Intell Human Comput

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Multilevel programming is widely applied to solve decentralized decision-making problems. In practice, indeterminacies are presented in these problems due to volatile factors or emergencies. As a type of indeterminacy, uncertainty is introduced in multilevel programming. For resolving multilevel programming problems with uncertain parameters, this paper constructs the uncertain expected value multilevel programming model and chance-constrained multilevel programming. Then, these models are converted to their equivalent forms. Moreover, the Stackelberg-Nash equilibrium solutions are obtained by using a genetic algorithm. Finally, these models are applied to the omni-channel vehicle routing problem, and a numerical experiment is given. The numerical experiment shows that the established models can optimize the distribution efficiency by coordinating the interests of decision-makers.

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“…It is the most widely used and mature clustering analysis method so far [26]. It is widely used in data processing [27], image processing [28], market analysis [29], and many other research fields [30]. The K-average clustering algorithm is the core thought in the center of the data set to choose K-class data clustering and the clustering center according to the calculated attribute value, and the value of the minimum Euclidean distance principle can be divided into K classes.…”

Section: Introductionmentioning

confidence: 99%

Failure Evaluation of Electronic Products Based on Double Hierarchy Hesitant Fuzzy Linguistic Term Set and K-Means Clustering Algorithm

et al. 2022

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The extent of failure evaluation depends on the complexity and significance of electronic products. However, traditional failure mode and effect analysis (FMEA) has many shortcomings, which brings large difficulty to failure evaluating work. This paper uses the double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) and the K-means clustering algorithm to improve the shortcomings of traditional FMEA. The DHHFLTS is a set of language terms based on the symmetry method and uniform language granularity. Firstly, we determine the product failure mode and set up an evaluation team after formulating an evaluation symmetrical language set. The psychological changes of the evaluators can be truly expressed by using the DHHFLTS. Secondly, the entropy weight method is used to calculate the weight of the evaluation members. The evaluation information of the evaluation personnel on the failure mode is aggregated based on the weight of the evaluation members. Then, the K-means clustering algorithm is used to calculate the distance between failure modes and each cluster center point by using the normalized weight of influencing factors and the evaluation distance of each evaluator. Finally, the evaluation of an electromagnet failure mode is taken as an example to prove the objectivity and practicability of the new method.

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K-means cluster interactive algorithm-based evolutionary approach for solving bilevel multi-objective programming problems

Cited by 27 publications

References 26 publications

Deep learning approach analysis model prediction and classification poverty status

Deep learning approach analysis model prediction and classification poverty status

Uncertain multilevel programming with application to omni-channel vehicle routing problem

Failure Evaluation of Electronic Products Based on Double Hierarchy Hesitant Fuzzy Linguistic Term Set and K-Means Clustering Algorithm

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