قارن الطرق
راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| خوارزمية القندس القزم المُحسِّنة (DMO)× | خوارزمية العفن الهلامي× | |
|---|---|---|
| المجال | التحسين | التحسين |
| العائلة | Machine learning | Machine learning |
| سنة النشأة≠ | 2022 | 2020 |
| صاحب الطريقة≠ | Joseph O. Agushaka | Shimin Li |
| النوع | Nature-inspired metaheuristic algorithm | Nature-inspired metaheuristic algorithm |
| المصدر التأسيسي≠ | 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 ↗ |
| الأسماء البديلة | DMO | SMA |
| ذات صلة≠ | 4 | 5 |
| الملخص≠ | 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. |
| ScholarGateمجموعة البيانات ↗ |
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