ACGRIME: adaptive chaotic Gaussian RIME optimizer for global optimization and feature selection

Feature selection (FS) is a crucial data preprocessing technique that selects important features to enhance learning efficiency, yet it encounters challenges due to the high-dimensional search space. This paper introduces an improved version of the RIME optimization algorithm called ACGRIME. Inspired by rime-ice formation, RIME initially showed good convergence and search capabilities but struggled with diversity and local optima in FS. ACGRIME integrates chaos theory, adaptive weighting, and Gaussian mutation to address these limitations to enhance exploration, balance exploration–exploitation, and improve solution quality. Extensive experiments were conducted using 29 CEC 2017 benchmark functions, comparing ACGRIME against the original RIME, two recently enhanced RIME variants, and 11 advanced metaheuristic algorithms. Further, balance and diversity analyses of ACGRIME, RIME, and each strategy were conducted to demonstrate its exploration and exploitation capabilities. Following this, ACGRIME is then adapted into its binary form, bACGRIME, to evaluate its performance in FS on 22 datasets, testing it with different machine learning classifiers and benchmarking it against six well-known algorithms. The experimental results on the benchmark functions, balance, and diversity analyses demonstrate that ACGRIME achieves better convergence speed and balanced exploration–exploitation. Moreover, bACGRIME’s performance for FS shows superior classification accuracy with fewer features. The source code of ACGRIME and bACGRIME is publicly accessible at https://github.com/batis1/ACGRIME.