Advanced RIME architecture for global optimization and feature selection

IF 8.6 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

Journal of Big Data Pub Date : 2024-06-18 DOI:10.1186/s40537-024-00931-8

Ruba Abu Khurma, Malik Braik, Abdullah Alzaqebah, Krishna Gopal Dhal, Robertas Damaševičius, Bilal Abu-Salih

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Abstract

The article introduces an innovative approach to global optimization and feature selection (FS) using the RIME algorithm, inspired by RIME-ice formation. The RIME algorithm employs a soft-RIME search strategy and a hard-RIME puncture mechanism, along with an improved positive greedy selection mechanism, to resist getting trapped in local optima and enhance its overall search capabilities. The article also introduces Binary modified RIME (mRIME), a binary adaptation of the RIME algorithm to address the unique challenges posed by FS problems, which typically involve binary search spaces. Four different types of transfer functions (TFs) were selected for FS issues, and their efficacy was investigated for global optimization using CEC2011 and CEC2017 and FS tasks related to disease diagnosis. The results of the proposed mRIME were tested on ten reliable optimization algorithms. The advanced RIME architecture demonstrated superior performance in global optimization and FS tasks, providing an effective solution to complex optimization problems in various domains.

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用于全局优化和特征选择的先进 RIME 架构

文章介绍了一种利用 RIME 算法进行全局优化和特征选择（FS）的创新方法，其灵感来自 RIME 冰的形成。RIME 算法采用了软 RIME 搜索策略和硬 RIME 穿刺机制，以及改进的正贪婪选择机制，以防止陷入局部最优并增强其整体搜索能力。文章还介绍了二进制修正 RIME（mRIME），这是对 RIME 算法的二进制调整，以应对 FS 问题带来的独特挑战，这些问题通常涉及二进制搜索空间。针对 FS 问题选择了四种不同类型的传递函数 (TF)，并利用 CEC2011 和 CEC2017 以及与疾病诊断相关的 FS 任务研究了它们在全局优化方面的功效。在十种可靠的优化算法上测试了所提出的 mRIME 的结果。先进的 RIME 架构在全局优化和 FS 任务中表现出卓越的性能，为不同领域的复杂优化问题提供了有效的解决方案。

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来源期刊

Journal of Big Data Computer Science-Information Systems

CiteScore

17.80

自引率

3.70%

发文量

105

审稿时长

13 weeks

期刊介绍： The Journal of Big Data publishes high-quality, scholarly research papers, methodologies, and case studies covering a broad spectrum of topics, from big data analytics to data-intensive computing and all applications of big data research. It addresses challenges facing big data today and in the future, including data capture and storage, search, sharing, analytics, technologies, visualization, architectures, data mining, machine learning, cloud computing, distributed systems, and scalable storage. The journal serves as a seminal source of innovative material for academic researchers and practitioners alike.