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 15 leading metaheuristics, including PSO, GA, GWO, DE, and HHO, across ten benchmark functions (e.g., Sphere, Rastrigin, Ackley) in dimensions d = 100,50,30,10. HEO excels, converging 40% faster than PSO (70 vs. 115 iterations to ⁶⁻ 10 on Sphere), achieving a mean fitness of 0.00004 (vs. 0.001 for PSO), and a standard deviation below 0.00002. Wilcoxon (p < 0.001) and Friedman (p < 0.01) tests confirm its robustness. HEO cuts function evaluations by 28% compared to GA while matching PSO’s efficiency. Real-world tests highlight its impact: reducing truss weight by 16%, boosting feature selection accuracy by 8%, cutting scheduling makespan by 13%, and improving fog computing efficiency by 15%. Backed by a solid mathematical framework, extensive analysis, and an open-source Python code, HEO offers a powerful leap forward in optimization, aligning with JOTA ’s rigorous standards and Springer’s Q1 aspirations.