An efficient discrete rat swarm optimizer for global optimization and feature selection in chemoinformatics

Machine learning algorithms need feature selection (FS) as a significant step toward filtering unnecessary data. This paper proposes a wrapper FS approach that combines the rat swarm optimization (RSO) algorithm with genetic operators to avoid local optimal. In the proposed approach the transfer functions (TFs) are added to balance local and global search by converting a continuous search space into a discrete space. Eight variants of the bmRSO algorithm were applied for classification purposes using a support vector machine (SVM) to increase accuracy and decrease the number of features over several chemical datasets. The eight bmRSO proposed methods and the original RSO were evaluated using the CEC’20 test suite and twelve datasets (eight chemical and four toxicity effect datasets) to verify their performance in complex optimization problems and FS over real datasets, respectively. Moreover, the binary versions of other stable metaheuristic algorithms such as Harris Hawks Optimization (HHO), Grey Wolf Optimization (GWO), Farmland Fertility Algorithm (FFA), Artificial Gorilla Troops Optimizer (GTO), African Vultures Optimization Algorithm (AVOA), Runge Kutta Optimizer’s (RUN), and Slime Mould Algorithm (SMA) were used to compare the results obtained by the best variant of the bmRSO. Eventually, the experimental results have revealed that in most of the tests, the proposed bmRSO1 has achieved efficient search results with higher convergence speeds without increasing additional computational efforts. From the twelve datasets, the MAO dataset reached the highest results compared with other datasets, so the proposed method, bmRSO1-SVM, achieved an accuracy of 98.201% and a 20.001 number of selected features.