विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| ड्वार्फ मंगूज ऑप्टिमाइज़ेशन× | स्लाइम मोल्ड एल्गोरिथम× | |
|---|---|---|
| क्षेत्र | अनुकूलन | अनुकूलन |
| परिवार | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 2022 | 2020 |
| प्रवर्तक≠ | Joseph O. Agushaka | Shimin Li |
| प्रकार | Nature-inspired metaheuristic algorithm | Nature-inspired metaheuristic algorithm |
| मौलिक स्रोत≠ | 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 ↗ |
| उपनाम | DMO | SMA |
| संबंधित≠ | 4 | 5 |
| सारांश≠ | 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. |
| ScholarGateडेटासेट ↗ |
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