Twin and multiple black holes algorithm for feature selection
Published in 2020 IEEE-HYDCON., 2020
The standard Black Hole algorithm uses a single attractor to guide the search, which can limit diversity and cause premature convergence. This paper introduces Twin and Multiple Black Hole variants that maintain several competing attractors simultaneously, improving exploration of the feature space. Paired with an SVM classifier, the new variants outperform the original algorithm on feature subset quality and classification accuracy.
Recommended citation: P. T. Ovhal, J. K. Valadi and A. Sane, "Twin and Multiple Black Holes Algorithm for Feature Selection," 2020 IEEE-HYDCON, Hyderabad, India, 2020, pp. 1-6, doi: 10.1109/HYDCON48903.2020.9242882. https://ieeexplore.ieee.org/abstract/document/9242882