ArticleiScience2024
IRIME: Mitigating exploitation-exploration imbalance in RIME optimization for feature selection.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A multi-strategy integrated improved nutcracker optimization algorithm and its applications in water conservancy engineering.iScience · 2026Article
- DRIME: A Distributed Data-Guided RIME Algorithm for Numerical Optimization Problems.Biomimetics (Basel, Switzerland) · 2025Article
- Multi-strategy enterprise development optimizer for numerical optimization and constrained problems.Scientific reports · 2025Article
- A Novel Hybrid Improved RIME Algorithm for Global Optimization Problems.Biomimetics (Basel, Switzerland) · 2024Article
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Authors and funding
6 authors.
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Abstract
Rime optimization algorithm (RIME) encounters issues such as an imbalance between exploitation and exploration, susceptibility to local optima, and low convergence accuracy when handling problems. This paper introduces a variant of RIME called IRIME to address these drawbacks. IRIME integrates the soft besiege (SB) and composite mutation strategy (CMS) and restart strategy (RS). To comprehensively validate IRIME's performance, IEEE CEC 2017 benchmark tests were conducted, comparing it against many advanced algorithms. The results indicate that the performance of IRIME is the best. In addition, applying IRIME in four engineering problems reflects the performance of IRIME in solving practical problems. Finally, the paper proposes a binary version, bIRIME, that can be applied to feature selection problems. bIRIMR performs well on 12 low-dimensional datasets and 24 high-dimensional datasets. It outperforms other advanced algorithms in terms of the number of feature subsets and classification accuracy. In conclusion, bIRIME has great potential in feature selection.
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