Harris Hawks Optimizer for High-Dimensional Biomarker Discovery: A Statistically Rigorous Comparative Study of Eleven Metaheuristic Algorithms on Leukemia Gene Expression Data

Authors

  • Zeinab Parandavar * Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran. https://orcid.org/0009-0003-1226-7466
  • Behnam Ghahramankhani Department of Computer and Information Technology Engineering, Ab.C., Abhar Branch, Islamic Azad University, Abhar, Iran.
  • Mohammad Hossein Khodabandeh Department of Computer and Information Technology Engineering, Ab.C., Abhar Branch, Islamic Azad University, Abhar, Iran.

https://doi.org/10.48313/maa.vi.81

Abstract

The rapid accumulation of high-throughput omics data has transformed disease diagnosis, yet the extreme dimensionality of gene expression profiles continues to impose severe computational and statistical obstacles for biomarker discovery. Although many nature-inspired metaheuristic algorithms have been proposed for Feature Selection (FS), the literature lacks rigorous, large-scale, statistically validated comparative studies on noisy, epistatic biological landscapes. This study presents an exhaustive evaluation of the Harris Hawks Optimizer (HHO) against ten state-of-the-art metaheuristics for binary FS  on the benchmark Leukemia (ALL versus AML) microarray dataset. A Binary HHO (BHHO) wrapper built around a Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel is benchmarked against ten competitors under strictly unified budgets of 30 agents, 100 iterations, 30 independent runs, and stratified 10-fold cross-validation. Algorithms are assessed on accuracy, F1-score, Matthews Correlation Coefficient (MCC), area under the curve, subset size, and runtime, with significance verified by Wilcoxon signed-rank and Friedman tests. HHO attained 98.61% accuracy, an Area Under the Curve (AUC) of 0.992, and an MCC of 0.971 using only 12.4 genes on average, significantly outperforming every competitor at p < 0.05 and ranking first under the Friedman test with a mean rank of 1.00. Biological enrichment analysis confirmed that the selected genes are established leukemia drivers. The dynamic escape energy mechanism of HHO provides an exceptional exploration-exploitation balance for high-dimensional biological FS, offering a reliable, interpretable, and clinically translatable biomarker discovery tool.

Keywords:

Feature selection, Harris hawks optimizer, Gene expression, Biomarker discovery, Leukemia classification, Wrapper methods

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Published

2026-03-04

Issue

Vol. 3 No. 1 (2026): Metaheuristic Algorithms with Applications (MAA)

Section

Articles

How to Cite

Parandavar, Z. ., Ghahramankhani, B. ., & Khodabandeh, M. H. . (2026). Harris Hawks Optimizer for High-Dimensional Biomarker Discovery: A Statistically Rigorous Comparative Study of Eleven Metaheuristic Algorithms on Leukemia Gene Expression Data. Metaheuristic Algorithms With Applications, 3(1), 91-110. https://doi.org/10.48313/maa.vi.81

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