Comparar métodos

	Optimización de Gavilanes de Harris	Optimización por Enjambre de Partículas (PSO)
Campo	Optimización	Optimización
Familia	Machine learning	Process / pipeline
Año de origen	2019	1995
Autor original	Ali Asghar Heidari	—
Tipo	Nature-inspired metaheuristic algorithm	Population-based metaheuristic / swarm intelligence
Fuente seminal	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.
Alias	HHO	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
Relacionados	4	6
Resumen	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.