Harpy Eagle Optimization: Bio-Inspired Metaheuristic for Complex Problems

This paper presents Harpy Eagle Optimization (HEO), a novel bio-inspired metaheuristic algorithm modeled on the hunting strategies of Harpy Eagles (Harpia harpyja), renowned for their agility and precision in rainforests. HEO uses a dual-phase approach—global exploration through soaring and local exploitation via targeted dives—to tackle high-dimensional, non-convex, and multi-modal optimization problems common in engineering, machine learning, and industry. We compare HEO against ۱۵ leading metaheuristics, including PSO, GA, GWO, DE, and HHO, across ten benchmark functions (e.g., Sphere, Rastrigin, Ackley) in dimensions d=۱۰۰,۵۰,۳۰,۱۰. HEO excels, converging ۴۰% faster than PSO (۷۰ vs. ۱۱۵ iterations to ۶-۱۰ on Sphere), achieving a mean fitness of ۰.۰۰۰۰۴ (vs. ۰.۰۰۱ for PSO), and a standard deviation below ۰.۰۰۰۰۲. Wilcoxon (p < ۰.۰۰۱) and Friedman (p < ۰.۰۱) tests confirm its robustness. HEO cuts function evaluations by ۲۸% compared to GA while matching PSO’s efficiency. Real-world tests highlight its impact: reducing truss weight by ۱۶%, boosting feature selection accuracy by ۸%, cutting scheduling makespan by ۱۳%, and improving fog computing efficiency by ۱۵%. Backed by a solid mathematical framework, extensive analysis, and an open-source Python code, HEO offers a powerful leap forward in optimization, aligning with SN Computer Science’s rigorous standards and Springer’s Q۱ aspirations.

کلیدواژه ها:

Metaheuristic optimization ، Harpy Eagle ، Nature-inspired ، Global optimization ، Convergence