Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Optimisation par essaim particulaire (PSO)× | Algorithme de la moisissure visqueuse× | |
|---|---|---|
| Domaine | Optimisation | Optimisation |
| Famille≠ | Process / pipeline | Machine learning |
| Année d'origine≠ | 1995 | 2020 |
| Auteur d'origine≠ | — | Shimin Li |
| Type≠ | Population-based metaheuristic / swarm intelligence | Nature-inspired metaheuristic algorithm |
| Source fondatrice≠ | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. 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 ↗ |
| Alias≠ | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) | SMA |
| Apparentées≠ | 6 | 5 |
| Résumé≠ | Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems. | 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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