Huang Jinpeng, Chen Yi, Heidari Ali Asghar, Liu Lei, Chen Huiling, Liang Guoxi
Institute of Big Data and Information Technology, Wenzhou University, Wenzhou 325000, China.
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
iScience. 2024 Jul 22;27(8):110561. doi: 10.1016/j.isci.2024.110561. eCollection 2024 Aug 16.
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.
rime优化算法（RIME）在处理问题时存在开发与探索不平衡、易陷入局部最优以及收敛精度低等问题。本文介绍了一种名为IRIME的RIME变体，以解决这些缺点。IRIME集成了软包围（SB）、复合变异策略（CMS）和重启策略（RS）。为了全面验证IRIME的性能，进行了IEEE CEC 2017基准测试，并将其与许多先进算法进行比较。结果表明，IRIME的性能最佳。此外，将IRIME应用于四个工程问题，体现了IRIME在解决实际问题方面的性能。最后，本文提出了一种可应用于特征选择问题的二进制版本bIRIME。bIRIMR在12个低维数据集和24个高维数据集上表现良好。在特征子集数量和分类精度方面，它优于其他先进算法。总之，bIRIME在特征选择方面具有巨大潜力。
