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	Harris Hawks Optimization	Ottimizzazione a Sciame di Particelle (PSO)	Algoritmo dello Striscio di Muffa
Campo	Ottimizzazione	Ottimizzazione	Ottimizzazione
Famiglia	Machine learning	Process / pipeline	Machine learning
Anno di origine	2019	1995	2020
Ideatore	Ali Asghar Heidari	—	Shimin Li
Tipo	Nature-inspired metaheuristic algorithm	Population-based metaheuristic / swarm intelligence	Nature-inspired metaheuristic algorithm
Fonte seminale	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.	Li, S., Chen, H., Wang, M., Heidari, A. A., & Chakraborty, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300-323.
Alias	HHO	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)	SMA
Correlati	4	6	5
Sintesi	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.	The Slime Mould Algorithm (SMA) is a nature-inspired metaheuristic optimization technique introduced by Li et al. in 2020. It mimics the behavior of slime moulds, which spread and contract to find optimal food sources. SMA addresses complex optimization problems by simulating the adaptive foraging and spatial distribution patterns of these organisms.