Abstract
In this study, the authors aimed to study an effective intelligent method for employment stability prediction in order to provide a reasonable reference for postgraduate employment decision and for policy formulation in related departments. First, this paper introduces an enhanced slime mould algorithm (MSMA) with a multi-population strategy. Moreover, this paper proposes a prediction model based on the modified algorithm and the support vector machine (SVM) algorithm called MSMA-SVM. Among them, the multi-population strategy balances the exploitation and exploration ability of the algorithm and improves the solution accuracy of the algorithm. Additionally, the proposed model enhances the ability to optimize the support vector machine for parameter tuning and for identifying compact feature subsets to obtain more appropriate parameters and feature subsets. Then, the proposed modified slime mould algorithm is compared against various other famous algorithms in experiments on the 30 IEEE CEC2017 benchmark functions. The experimental results indicate that the established modified slime mould algorithm has an observably better performance compared to the algorithms on most functions. Meanwhile, a comparison between the optimal support vector machine model and other several machine learning methods on their ability to predict employment stability was conducted, and the results showed that the suggested the optimal support vector machine model has better classification ability and more stable performance. Therefore, it is possible to infer that the optimal support vector machine model is likely to be an effective tool that can be used to predict employment stability.
Highlights
In China, postgraduates are valuable talent resources
The simulation results reveal the postgraduate student employment stability is influenced by the constraints of many factors, showing corresponding patterns in specific aspects and showing some inevitable links with most of the factors involved
This section analyzes and predicts graduate student employment stability based on these five characteristic factors while further demonstrating the practical significance and validity of the modified slime mould algorithm (MSMA)-support vector machine (SVM) model
Summary
In China, postgraduates are valuable talent resources. The employment quality of postgraduates is related to their own sense of social belonging and security, but it affects social stability and sustainable development, where employment stability is an important measure of postgraduate employment quality. For enterprises, if they can retain talent and maintain the job stability of new graduate students, they can reduce labor costs, but these enterprises can achieve sustainable development. It is necessary to analyze the employment stability of graduate students through the effective mining of big data related to post-graduation graduate employment and to construct an intelligent prediction model using a fusion of intelligent optimization algorithms and machine learning methods to verify the hypothesis of relevant relationships. The core principle of SVMs is the development of a plane that is best able to divide two kinds of data in such a way where the distance between the two is maximized and where the classification has the greatest generalization power.
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