Attribution-Stability-Driven Harris Hawks Search for Interpretable Clinical Feature Selection

Authors

  • Seyed Farid Ghannadpour * Department of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran. https://orcid.org/0000-0002-5956-1262
  • Daniel A. Ferreira School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil.

https://doi.org/10.48313/maa.vi.75

Abstract

This paper introduces Attribution-Stability-Driven Harris Hawks Search (ASD-HHS), a novel metaheuristic optimization algorithm designed to address mixed-integer nonlinear programming. The proposed approach leverages Harris Hawks Optimization (HHO) + SHAP-gradient local refinement to achieve robust and efficient performance across diverse problem instances. Unlike existing methods that rely on fixed search operators and static parameter configurations, ASD-HHS incorporates adaptive mechanisms that dynamically adjust the search strategy based on real-time landscape analysis. We provide a rigorous theoretical framework establishing convergence guarantees under mild assumptions, along with a detailed complexity analysis demonstrating the algorithm's computational efficiency. The experimental evaluation employs Sobol [1] sensitivity analysis, Morris [2] elementary effects, one-at-a-time perturbation, featuring global sensitivity analysis of algorithmic parameters. Statistical significance is assessed using Bayesian signed-rank + bootstrap CI, with effect size reporting to quantify practical significance. Results demonstrate that ASD-HHS achieves statistically significant improvements over nine state-of-the-art baselines, with an average performance gain of 22.5% and large effect sizes (Cohen's d > 0.8). Ablation studies confirm the contribution of each algorithmic component, and sensitivity analysis identifies the most influential parameters. The framework is validated on real-world problem instances, demonstrating practical applicability and robustness under varying conditions.

Keywords:

Attribution-stability-driven Harris Hawks search, Mixed-integer nonlinear programming, Harris Hawks optimization + SHAP-gradien, Global sensitivity analysis of algorithm, Metaheuristic optimization

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Published

2026-06-04

Issue

Vol. 3 No. 2 (2026): Metaheuristic Algorithms with Applications (MAA)

Section

Articles

How to Cite

Ghannadpour, S. F. ., & Ferreira, D. A. . (2026). Attribution-Stability-Driven Harris Hawks Search for Interpretable Clinical Feature Selection. Metaheuristic Algorithms With Applications, 3(2), 227-245. https://doi.org/10.48313/maa.vi.75

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