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| Runge-Kutta-Optimierer× | Slime Mould Algorithm× | |
|---|---|---|
| Fachgebiet | Optimierung | Optimierung |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2023 | 2020 |
| Urheber≠ | Ayushi Khatri | Shimin Li |
| Typ≠ | Mathematical metaheuristic algorithm | Nature-inspired metaheuristic algorithm |
| Wegweisende Quelle≠ | Khatri, A., Kumar, A., & Gaba, G. K. (2023). Runge Kutta optimizer: An efficient approach for solving optimization tasks. Computers and Industrial Engineering, 180, 109201. link ↗ | 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. DOI ↗ |
| Aliasnamen | RKO | SMA |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | The Runge Kutta Optimizer (RKO) is a metaheuristic algorithm introduced by Khatri et al. in 2023 that leverages numerical integration principles from the Runge-Kutta method. Instead of biological inspiration, RKO grounds optimization in mathematical principles of differential equations and numerical integration. The algorithm treats the optimization landscape as a dynamic system and uses multi-stage integration steps to evolve solutions toward optima. | 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. |
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