So sánh phương pháp

	Tối ưu hóa Cầy Mangut Lùn	Tối ưu hóa Diều Harris
Lĩnh vực	Tối ưu hóa	Tối ưu hóa
Họ	Machine learning	Machine learning
Năm ra đời	2022	2019
Người khởi xướng	Joseph O. Agushaka	Ali Asghar Heidari
Loại	Nature-inspired metaheuristic algorithm	Nature-inspired metaheuristic algorithm
Công trình gốc	Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 391, 114570.	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.
Tên gọi khác	DMO	HHO
Liên quan	4	4
Tóm tắt	The Dwarf Mongoose Optimization (DMO) algorithm is a nature-inspired metaheuristic introduced by Agushaka et al. in 2022, based on the behavioral patterns of dwarf mongoose colonies. Dwarf mongooses exhibit sophisticated group dynamics including sentry behavior (surveillance and exploration), pup care (mentoring), and cooperative hunting. The algorithm translates these social behaviors into optimization mechanisms that balance exploration and exploitation effectively.	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.