قارن الطرق

	خوارزمية القندس القزم المُحسِّنة (DMO)	تحسين صقور هاريس
المجال	التحسين	التحسين
العائلة	Machine learning	Machine learning
سنة النشأة	2022	2019
صاحب الطريقة	Joseph O. Agushaka	Ali Asghar Heidari
النوع	Nature-inspired metaheuristic algorithm	Nature-inspired metaheuristic algorithm
المصدر التأسيسي	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.
الأسماء البديلة	DMO	HHO
ذات صلة	4	4
الملخص	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.