Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

Gao, Hongxing; Liang, Guoxi; Chen, Huiling

doi:10.3390/electronics11020209

Cited by 13 publications

(7 citation statements)

References 105 publications

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“…Yuheng Guo et al [87] employed the SMA approach to optimize the parameters of an SVM for an ancient glass classification. Gao H et al [88] proposed a prediction model on the basis of a modified SMA and SVM algorithm. Javidan SM et al [89] combined an SMA with an SVM classifier to diagnose apple tree diseases.…”

Section: Hybridization With the Support Vector Machine (Svm)mentioning

confidence: 99%

Advances in Slime Mould Algorithm: A Comprehensive Survey

Wei,

Othman,

Daud

et al. 2024

Biomimetics

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The slime mould algorithm (SMA) is a new swarm intelligence algorithm inspired by the oscillatory behavior of slime moulds during foraging. Numerous researchers have widely applied the SMA and its variants in various domains in the field and proved its value by conducting various literatures. In this paper, a comprehensive review of the SMA is introduced, which is based on 130 articles obtained from Google Scholar between 2022 and 2023. In this study, firstly, the SMA theory is described. Secondly, the improved SMA variants are provided and categorized according to the approach used to apply them. Finally, we also discuss the main applications domains of the SMA, such as engineering optimization, energy optimization, machine learning, network, scheduling optimization, and image segmentation. This review presents some research suggestions for researchers interested in this algorithm, such as conducting additional research on multi-objective and discrete SMAs and extending this to neural networks and extreme learning machining.

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Section: Hybridization With the Support Vector Machine (Svm)mentioning

confidence: 99%

Advances in Slime Mould Algorithm: A Comprehensive Survey

Wei,

Othman,

Daud

et al. 2024

Biomimetics

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“…Gao et al [6] introduced an enhanced slime mould algorithm (MSMA) with a multipopulation strategy and proposed a prediction model based on the modified algorithm and the support vector machine (SVM) algorithm called MSMA-SVM to provide a reference for postgraduate employment decision and policy formulation. The multi-population strategy improved the solution accuracy of the algorithm and the proposed model enhanced the ability to optimize the SVM.…”

Section: Evolutionary Computationmentioning

confidence: 99%

Advanced Machine Learning Applications in Big Data Analytics

2023

Electronics

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“…In addition, some scholars use machine learning algorithms to predict human behavior and its infuencing factors. For example, Gao et al [10], Tang et al [11], Wei et al [12], and Wang et al [13]. Tese methods provide many ideas for the application of machine learning algorithms in this research.…”

Section: Introductionmentioning

confidence: 99%

Influencing Factors of Employee Turnover Based on the Random Forest and DEMATEL Method: Evidence from the Qinghai Electric Power Company in China

Zheng

Liu

et al. 2023

Mathematical Problems in Engineering

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Under the background of the deepening reform of China’s electric power industry, how to make use of existing resources, keep existing employees, and identify and distinguish the influencing factors of turnover have become major problems faced by companies. At the same time, the identification of influencing factors also provides a reference for relevant enterprises to do a good job in talent reserve and recruitment. The influencing factors of employee turnover are numerous and interrelated. Identifying and distinguishing the influencing factors are the focus of academic research. Meanwhile, it provides a reference for relevant enterprises to do an excellent job in personnel reserve and recruitment. Aiming at the limitation of constructing a direct matrix based on experts’ scoring method in the traditional DEMATEL model, a model combining random forest and DEMATEL is proposed, applied to identify the influencing factors of employee turnover in electric power enterprises in Qinghai Province. Based on the employee turnover data set of Qinghai Electric Power Company from 2012 to 2020, the empirical analysis is conducted to analyze the influencing factors of employee turnover from two perspectives of the degree of centrality and the degree of causal. The research results show that marital status, household registration, political status, type of job, and the location of the work unit are the key influencing factors, providing a theoretical basis for the early warning of employee turnover and confirming the feasibility of the random forest-DEMATEL method proposed by us.

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Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

Cited by 13 publications

References 105 publications

Advances in Slime Mould Algorithm: A Comprehensive Survey

Advances in Slime Mould Algorithm: A Comprehensive Survey

Advanced Machine Learning Applications in Big Data Analytics

Influencing Factors of Employee Turnover Based on the Random Forest and DEMATEL Method: Evidence from the Qinghai Electric Power Company in China

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