Prey capture enhanced Harris hawks optimizer for wrapper-based feature selection in high-dimensional medical data

While the Harris Hawks Optimizer (HHO) is widely utilized for wrapper-based Feature Selection (FS) due to its efficiency and ease of implementation, existing HHO-based FS approaches encounter challenges when handling high-dimensional datasets, such as falling into local optima and high computational costs. In the HHO algorithm, the Harris hawks engage in surprise attacks on the identified prey according to the prey's escape energy. However, there may be scenarios where the prey could escape due to the algorithm's limitations. To enhance the algorithm's prey-capture ability, this article introduces an enhanced HHO algorithm termed Prey Capture Harris Hawks Optimizer (PCHHO).

The prey capture strategy incorporates crossover and mutation operators to enhance the algorithm's exploratory-exploitative capabilities. The performance of PCHHO is evaluated on the CEC2017 benchmark suite, where it is compared to HHO, with three enhanced HHO algorithms, nine classical metaheuristic algorithms, and nine improved metaheuristic algorithms. The experimental comparison results are synthesized using the Wilcoxon signed-rank and Friedman tests. Ultimately, a binary form of PCHHO (bPCHHO) is designed for wrapper-based FS and compared with six excellent binary metaheuristics using 15 high-dimensional medical datasets.

The results demonstrate the excellent performance of the proposed algorithm on the CEC2017 benchmark suite compared to other algorithms, as well as the effectiveness of bPCHHO in evolving a subset of features with 77% reduction in classification error, 8% reduction in computational time, and 73% fewer features selected compared to bHHO.

The proposed PCHHO and its binary variant bPCHHO exhibit superior performance in both benchmark optimization and wrapper-based FS for high-dimensional medical data, highlighting their potential for practical applications.