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<p>The standard Harris Hawks Optimization (HHO) algorithm often suffers from premature convergence and stagnation on complex, high-dimensional problems, particularly in wrapper-based feature selection (FS) for medical diagnosis, where costly fitness evaluations demand efficient search strategies. This paper proposes a Modified HHO (MHHO) that integrates three synergistic mechanisms: (i) Leader-Guided Perching (LGP), which introduces leader bias during exploration to accelerate movement toward promising regions; (ii) an Adaptive Deception Factor (ADF), which dynamically scales Lévy-flight intensity to escape deceptive local optima; and (iii) a Hierarchical Attack Strategy (HAS), which replaces the hard besiege phase with a mentor-guided exploitation step to mitigate swarm clumping and refine local search. A dual-domain evaluation was conducted. In Experiment Series 1, MHHO statistically outperformed the standard HHO on 18 of 23 benchmark functions, at the expected cost of higher computational time. In Experiment Series 2, involving wrapper-based FS on 15 medical datasets, MHHO achieved higher mean classification accuracy on most datasets while exhibiting enhanced stability, resulting in a favorable accuracy–sparsity trade-off through minimal yet high-performing feature subsets. Friedman’s test confirmed a significant global difference (p = 0.0013), and Holm’s post-hoc analysis verified that LGP, ADF, and HAS each contribute significant improvements over standard HHO. Overall, MHHO ranked among the top performers on approximately half of the evaluated benchmarks, demonstrating its effectiveness as a robust and accurate optimizer.</p>

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A multi-strategy framework for enhancing Harris hawks optimization for global optimization problems

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月30日

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  • Safaa Al-Adwan,
  • Salwani Abdullah,
  • Mohammed Alweshah,
  • Sofian Kassaymeh,
  • Aws Al-Qaisi,
  • Maen Takruri,
  • WANDEEP KAUR RATAN SINGH

摘要

The standard Harris Hawks Optimization (HHO) algorithm often suffers from premature convergence and stagnation on complex, high-dimensional problems, particularly in wrapper-based feature selection (FS) for medical diagnosis, where costly fitness evaluations demand efficient search strategies. This paper proposes a Modified HHO (MHHO) that integrates three synergistic mechanisms: (i) Leader-Guided Perching (LGP), which introduces leader bias during exploration to accelerate movement toward promising regions; (ii) an Adaptive Deception Factor (ADF), which dynamically scales Lévy-flight intensity to escape deceptive local optima; and (iii) a Hierarchical Attack Strategy (HAS), which replaces the hard besiege phase with a mentor-guided exploitation step to mitigate swarm clumping and refine local search. A dual-domain evaluation was conducted. In Experiment Series 1, MHHO statistically outperformed the standard HHO on 18 of 23 benchmark functions, at the expected cost of higher computational time. In Experiment Series 2, involving wrapper-based FS on 15 medical datasets, MHHO achieved higher mean classification accuracy on most datasets while exhibiting enhanced stability, resulting in a favorable accuracy–sparsity trade-off through minimal yet high-performing feature subsets. Friedman’s test confirmed a significant global difference (p = 0.0013), and Holm’s post-hoc analysis verified that LGP, ADF, and HAS each contribute significant improvements over standard HHO. Overall, MHHO ranked among the top performers on approximately half of the evaluated benchmarks, demonstrating its effectiveness as a robust and accurate optimizer.