WHHO: enhanced Harris hawks optimizer for feature selection in high-dimensional data

The Harris Hawks optimization (HHO) algorithm, categorized as a powerful meta-heuristic algorithm (MA), has gained extensive usage because it lacks parameter adjustment and shows powerful optimization ability. At the same time, it cannot be ignored within the realm of feature selection. However, HHO is deficient in its capacity to escape local optima, which deserves attention. HHO did not perform enough local search in the exploitation stage, resulting in poor final results. This limitation occurs from time to time when facing the high-dimensional feature selection. To address these concerns, a modification of HHO incorporating the wormhole strategy (WS) is proposed, called WHHO. As an example of local search, WS can mutate around the current and optimal search positions. By ensuring population diversity and enhancing the algorithm's capability to escape from local optima, WS contributes to improving results accuracy. WHHO first conducted qualitative experiments at CEC 2017, including historical trajectory analysis, balance analysis, and diversity analysis. Then, WHHO was compared with seven classical MAs, seven advanced MAs, and five well-known HHO variants. In the three groups of experiments, careful consideration of mean, standard deviation, Friedman test, and Wilcoxon sign-rank test results reveals that WHHO secures the top ranking. Furthermore, the binary variant of the algorithm, BWHHO, combined with the k nearest neighbor classifier, was developed for high-dimensional feature selection. Compared with nine binary MAs, WHHO uses fewer features to predict more accurate results on twelve high-dimensional datasets. The fitness value of WHHO on twelve datasets won the overwhelming first place, surpassing the second place with an average ranking of 2.5833. WHHO's ability to reduce the number of selected features is particularly impressive while maintaining high prediction accuracy. In the face of thousands of features, WHHO consistently selects fewer features, with retention rates below 20% on all datasets. This highlights WHHO's optimized feature selection ability and its effectiveness in simplifying the prediction process. Therefore, WHHO is an effective improved optimizer, especially for dealing with high-dimensional feature selection, which has effective value.