Abstract：
The RIME optimization algorithm is a newly developed physics-based optimization algorithm used for solving optimization problems. The RIME algorithm proved high-performing in various fields and domains, providing a high-performance solution. Nevertheless, like many swarm-based optimization algorithms, RIME suffers from many limitations, including the exploration-exploitation balance not being well balanced. In addition, the likelihood of falling into local optimal solutions is high, and the convergence speed still needs some work. Hence, there is room for enhancement in the search mechanism so that various search agents can discover new solutions. The authors suggest an adaptive chaotic version of the RIME algorithm named ACRIME, which incorporates four main improvements, including an intelligent population initialization using chaotic maps, a novel adaptive modified Symbiotic Organism Search (SOS) mutualism phase, a novel mixed mutation strategy, and the utilization of restart strategy. The main goal of these improvements is to improve the variety of the population, achieve a better balance between exploration and exploitation, and improve RIME\'s local and global search abilities. The study assesses the effectiveness of ACRIME by using the standard benchmark functions of the CEC2005 and CEC2019 benchmarks. The proposed ACRIME is also applied as a feature selection to fourteen various datasets to test its applicability to real-world problems. Besides, the ACRIME algorithm is applied to the COVID-19 classification real problem to test its applicability and performance further. The suggested algorithm is compared to other sophisticated classical and advanced metaheuristics, and its performance is assessed using statistical tests such as Wilcoxon rank-sum and Friedman rank tests. The study demonstrates that ACRIME exhibits a high level of competitiveness and often outperforms competing algorithms. It discovers the optimal subset of features, enhancing the accuracy of classification and minimizing the number of features employed. This study primarily focuses on enhancing the equilibrium between exploration and exploitation, extending the scope of local search.
摘要：
RIME优化算法是一种新开发的基于物理的优化算法，用于解决优化问题。RIME算法在各个领域和领域都表现良好，提供高性能解决方案。然而，像许多基于群体的优化算法一样，RIME受到许多限制，包括勘探-开发平衡不够平衡。此外,陷入局部最优解的可能性很高，和收敛速度仍然需要一些工作。因此，搜索机制还有增强的空间，以便各种搜索代理可以发现新的解决方案。作者提出了一种名为ACRIME的RIME算法的自适应混沌版本，其中包含四个主要改进，包括使用混沌地图的智能种群初始化，一种新的自适应改进的共生生物搜索(SOS)共生阶段，一种新的混合突变策略，以及重启策略的利用。这些改进的主要目标是改善人口的多样性，在勘探和开发之间取得更好的平衡，并提高RIME的本地和全球搜索能力。该研究通过使用CEC2005和CEC2019基准的标准基准函数来评估ACRIME的有效性。所提出的ACRIME也被用作14个不同数据集的特征选择，以测试其对现实问题的适用性。此外,将ACRIME算法应用于COVID-19分类实际问题，进一步测试其适用性和性能。将建议的算法与其他复杂的经典和高级元启发式算法进行比较，并使用统计检验如Wilcoxon秩和和和Friedman秩检验来评估其性能。研究表明，ACRIME具有很高的竞争力，并且通常优于竞争算法。它发现了特征的最佳子集，提高分类的准确性并最大程度地减少所采用的特征数量。这项研究主要侧重于加强勘探和开发之间的平衡，扩大本地搜索的范围。
