مقایسهٔ روش‌ها

	بهینه‌ساز رانگ-کوتا	بهینه‌سازی شاهین هریس
حوزه	بهینه‌سازی	بهینه‌سازی
خانواده	Machine learning	Machine learning
سال پیدایش	2023	2019
پدیدآور	Ayushi Khatri	Ali Asghar Heidari
نوع	Mathematical metaheuristic algorithm	Nature-inspired metaheuristic algorithm
منبع بنیادین	Khatri, A., Kumar, A., & Gaba, G. K. (2023). Runge Kutta optimizer: An efficient approach for solving optimization tasks. Computers and Industrial Engineering, 180, 109201.	Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872.
نام‌های دیگر	RKO	HHO
مرتبط	5	4
خلاصه	The Runge Kutta Optimizer (RKO) is a metaheuristic algorithm introduced by Khatri et al. in 2023 that leverages numerical integration principles from the Runge-Kutta method. Instead of biological inspiration, RKO grounds optimization in mathematical principles of differential equations and numerical integration. The algorithm treats the optimization landscape as a dynamic system and uses multi-stage integration steps to evolve solutions toward optima.	Harris Hawks Optimization (HHO) is a metaheuristic algorithm introduced by Heidari et al. in 2019, inspired by the hunting strategies of Harris's hawks. The algorithm models the cooperative hunting behavior and escape strategies of these raptors to solve complex optimization problems. HHO balances exploration through perching and exploitation through dynamic pursuit, making it effective for multimodal and high-dimensional optimization.