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Healing Intelligence: A Bio-Inspired Metaheuristic Optimization Method Using Recovery Dynamics

Author

Listed:
  • Vasileios Charilogis

    (Department of Informatics and Telecommunications, University of Ioannina, 47150 Kostaki Artas, Greece)

  • Ioannis G. Tsoulos

    (Department of Informatics and Telecommunications, University of Ioannina, 47150 Kostaki Artas, Greece)

Abstract

BioHealing Optimization (BHO) is a bio-inspired metaheuristic that operationalizes the injury–recovery paradigm through an iterative loop of recombination, stochastic injury, and guided healing. The algorithm is further enhanced by adaptive mechanisms, including scar map, hot-dimension focusing, RAGE/hyper-RAGE bursts (Rapid Aggressive Global Exploration), and healing-rate modulation, enabling a dynamic balance between exploration and exploitation. Across 17 benchmark problems with 30 runs, each under a fixed budget of 1.5 · 10 5 function evaluations, BHO achieves the lowest overall rank in both the “best-of-runs” (47) and the “mean-of-runs” (48), giving an overall rank sum of 95 and an average rank of 2.794. Representative first-place results include Frequency-Modulated Sound Waves, the Lennard–Jones potential, and Electricity Transmission Pricing. In contrast to prior healing-inspired optimizers such as Wound Healing Optimization (WHO) and Synergistic Fibroblast Optimization (SFO), BHO uniquely integrates (i) an explicit tri-phasic architecture (DE/best/1/bin recombination → Gaussian/Lévy injury → guided healing), (ii) per-dimension stateful adaptation (scar map, hot-dims), and (iii) stagnation-triggered bursts (RAGE/hyper-RAGE). These features provide a principled exploration–exploitation separation that is absent in WHO/SFO.

Suggested Citation

  • Vasileios Charilogis & Ioannis G. Tsoulos, 2025. "Healing Intelligence: A Bio-Inspired Metaheuristic Optimization Method Using Recovery Dynamics," Future Internet, MDPI, vol. 17(10), pages 1-46, September.
  • Handle: RePEc:gam:jftint:v:17:y:2025:i:10:p:441-:d:1759808
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    References listed on IDEAS

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    1. Trevor Hastie & Robert Tibshirani, 1987. "Non‐Parametric Logistic and Proportional Odds Regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 260-276, November.
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