Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Aritmētiskās optimizācijas algoritms× | Diferenciālā evolūcija× | |
|---|---|---|
| Nozare | Optimizācija | Optimizācija |
| Saime≠ | Machine learning | Process / pipeline |
| Izcelsmes gads≠ | 2020 | 1997 |
| Autors≠ | Laith Abualigah | Rainer Storn & Kenneth Price |
| Tips≠ | Mathematical metaheuristic algorithm | Population-based stochastic metaheuristic |
| Pirmavots≠ | Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Arithmetic optimization algorithm: A new metaheuristic algorithm for solving optimization problems. Applied Mathematics and Computation, 392, 125450. link ↗ | 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 ↗ |
| Citi nosaukumi≠ | AOA | DE algorithm, Diferansiyel Evrim (DE), DE optimization |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | The Arithmetic Optimization Algorithm (AOA) is a metaheuristic optimization approach introduced by Abualigah et al. in 2020 that leverages mathematical operators (multiplication, division, addition, subtraction) as the inspiration for search strategies. Unlike nature-inspired algorithms, AOA uses the inherent properties of arithmetic operations to balance exploration and exploitation, making it particularly effective for mathematical optimization problems. | 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. |
| ScholarGateDatu kopa ↗ |
|
|