방법 비교

	Harris Hawks Optimization	입자 군집 최적화 (PSO)
분야	최적화	최적화
계열	Machine learning	Process / pipeline
기원 연도	2019	1995
창시자	Ali Asghar Heidari	—
유형	Nature-inspired metaheuristic algorithm	Population-based metaheuristic / swarm intelligence
원전	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.	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
별칭	HHO	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
관련	4	6
요약	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.	Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems.