Improved RIME algorithm: CEC performance analysis and feature selection experiment of Sino foreign cooperative data sets

Abstract In recent years, with the rapid development of higher education, China has actively constructed an evaluation framework of education quality. As an important part of higher education, Sino foreign cooperation in running schools plays an important role in the development of higher education. The newly released evaluation criteria for Sino foreign cooperative education cover a series of influencing factors. However, objectively determining which of these factors are crucial for the success of Sino foreign cooperative education is essential for strengthening its future development. To address this challenge, we propose an adaptive hunting mechanism that utilizes the latest RIME algorithm and is enhanced through a criss-crossing mechanism, as well as a new ACRIME algorithm. We conducted a comparative analysis of ACRIME, the original RIME, and several other highly acclaimed improved algorithms. The results indicate that ACRIME exhibits excellent performance in multiple benchmark tests. Subsequently, we applied the ACRIME algorithm to cluster the dataset of Sino foreign cooperative education, and then used the binary version of ACRIME (bACRIME) for feature selection. In the tenfold cross validation, more than half of the selected features were repeatedly identified, indicating their potential correlation with the development of Sino foreign cooperative education. Therefore, it is necessary to pay more attention to these influential indicators to support future development efforts.