Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Otimização Harris Hawks (HHO)× | Otimização por Enxame de Partículas (PSO)× | |
|---|---|---|
| Área | Otimização | Otimização |
| Família≠ | Machine learning | Process / pipeline |
| Ano de origem≠ | 2019 | 1995 |
| Autor original≠ | Ali Asghar Heidari | — |
| Tipo≠ | Nature-inspired metaheuristic algorithm | Population-based metaheuristic / swarm intelligence |
| Fonte 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. DOI ↗ | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ |
| Outros nomes≠ | HHO | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) |
| Relacionados≠ | 4 | 6 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
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