方法对比

	Harris Hawks Optimization	灰狼优化算法
领域	优化	优化
方法族	Machine learning	Process / pipeline
起源年份	2019	2014
提出者	Ali Asghar Heidari	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis
类型	Nature-inspired metaheuristic algorithm	Swarm-intelligence metaheuristic
开创性文献	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.	Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.
别名	HHO	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)
相关	4	5
摘要	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.	The Grey Wolf Optimizer (GWO) is a swarm-intelligence metaheuristic introduced by Mirjalili, Mirjalili, and Lewis in 2014 that models the social hierarchy and cooperative hunting behaviour of grey wolves. A population of candidate solutions is divided into four leadership ranks — alpha, beta, delta, and omega — and the three best solutions at each iteration guide the entire swarm toward increasingly better regions of the search space.