EDRIME: combining exponential distribution optimizer and directed crossover for feature selection

The Rime Optimization Algorithm (RIME) is a novel metaheuristic approach inspired by the natural formation of rime ice. Despite being a metaheuristic algorithm, RIME still exhibits limitations such as slow convergence speed, low convergence accuracy, and vulnerability to local optima. To overcome its drawbacks, this paper introduces an improved RIME algorithm, EDRIME. The EDRIME algorithm integrates an Exponential Distribution Optimizer (EDO) and a Directional Crossover (DX) strategy to accelerate the convergence rate and enhance global search capabilities. This paper compares EDRIME with 16 advanced algorithms on the CEC2014 and CEC2022 benchmarks to validate its performance in global optimization, including advanced improved algorithms such as EBOwithCMAR and LSHADE_cnEpSi. Experiments have shown that EDRIME outperforms other comparative algorithms in different problem dimensions and testing benchmarks. With the continuous expansion of data scale, the cost and complexity of solving Feature Selection (FS) problems are increasing. To address the challenge of high-dimensional feature selection, this study introduces a novel method named BEDRIME-KELM, which integrates EDRIME with the KELM classifier. The efficacy of the method is validated across 12 diverse datasets sourced from the UCI repository. The results demonstrate that BEDRIME-KELM exhibits strong competitiveness in classification accuracy and feature reduction.