Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Evolução Diferencial× | Otimizador Lobo Cinzento× | |
|---|---|---|
| Área | Otimização | Otimização |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 1997 | 2014 |
| Autor original≠ | Rainer Storn & Kenneth Price | Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis |
| Tipo≠ | Population-based stochastic metaheuristic | Swarm-intelligence metaheuristic |
| Fonte seminal≠ | Storn, R. & Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341–359. DOI ↗ | Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61. DOI ↗ |
| Outros nomes | DE algorithm, Diferansiyel Evrim (DE), DE optimization | GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO) |
| Relacionados | 5 | 5 |
| Resumo≠ | Differential Evolution (DE), introduced by Rainer Storn and Kenneth Price in 1997, is a population-based stochastic optimisation algorithm designed for continuous parameter spaces. It generates candidate solutions by combining vector differences between existing population members, making it a powerful and parameter-lean alternative to Genetic Algorithms and Particle Swarm Optimisation when the search landscape is non-convex, multimodal, or poorly suited to gradient-based methods. | 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. |
| ScholarGateConjunto de dados ↗ |
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