A hierarchical multi-leadership sine cosine algorithm to dissolving global optimization and data classification: The COVID-19 case study

• Published in: Computers in biology and medicine Vol. 164; p. 107212
• Main Authors: Zhong, Mingyang, Wen, Jiahui, Ma, Jingwei, Cui, Hao, Zhang, Qiuling, Parizi, Morteza Karimzadeh
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.09.2023; Elsevier Limited
• Subjects: Algorithms; Classification; COVID-19; Data classification; Datasets; Feature weighting; Global optimization; Heuristic methods; Internal Medicine; Leadership; Other; Search methods; Search process; Sine cosine algorithm; Support vector machine; Support vector machines; Trigonometric functions; COVID-19; Data classification; Sine cosine algorithm; Support vector machine; Feature weighting
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2023.107212

The Sine Cosine Algorithm (SCA) is an outstanding optimizer that is appreciably used to dissolve complicated real-world problems. Nevertheless, this algorithm lacks sufficient population diversification and a sufficient balance between exploration and exploitation. So, effective techniques are required to tackle the SCA’s fundamental shortcomings. Accordingly, the present paper suggests an improved version of SCA called Hierarchical Multi-Leadership SCA (HMLSCA) which uses an effective hierarchical multi-leadership search mechanism to lead the search process on multiple paths. The efficiency of the HMLSCA has been appraised and compared with a set of famous metaheuristic algorithms to dissolve the classical eighteen benchmark functions and thirty CEC 2017 test suites. The results demonstrate that the HMLSCA outperforms all compared algorithms and that the proposed algorithm provided a promising efficiency. Moreover, the HMLSCA was applied to handle the medicine data classification by optimizing the support vector machine’s (SVM) parameters and feature weighting in eight datasets. The experiential outcomes verify the productivity of the HMLSCA with the highest classification accuracy and a gain scoring 1.00 Friedman mean rank versus the other evaluated metaheuristic algorithms. Furthermore, the proposed algorithm was used to diagnose COVID-19, in which it attained the topmost accuracy of 98% in diagnosing the infection on the COVID-19 dataset, which proves the performance of the proposed search strategy. •In this article proposed a new improved sine cosine algorithm (HMLSCA).•Introducing a hierarchical multi-leadership search strategy in HMLSCA.•The proposed algorithm is evaluated on the 28 benchmark functions including unimodal, multimodal, and CEC 2019 functions.•The HMLSCA is applied for feature weighting and SVM’s parameter optimizing to handle data classification problems on several real-world medicine datasets.•The HMLSCA is used to diagnose the coronavirus (COVID-19) illness.