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| Dwarf Mongoose Optimization× | Slime Mould Algorithm× | |
|---|---|---|
| Fachgebiet | Optimierung | Optimierung |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2022 | 2020 |
| Urheber≠ | Joseph O. Agushaka | Shimin Li |
| Typ | Nature-inspired metaheuristic algorithm | Nature-inspired metaheuristic algorithm |
| Wegweisende Quelle≠ | Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 391, 114570. DOI ↗ | 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 | DMO | SMA |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | The Dwarf Mongoose Optimization (DMO) algorithm is a nature-inspired metaheuristic introduced by Agushaka et al. in 2022, based on the behavioral patterns of dwarf mongoose colonies. Dwarf mongooses exhibit sophisticated group dynamics including sentry behavior (surveillance and exploration), pup care (mentoring), and cooperative hunting. The algorithm translates these social behaviors into optimization mechanisms that balance exploration and exploitation effectively. | 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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