Comparar métodos
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| Optimizador de Búsqueda de Medusas× | Optimización por Enjambre de Partículas (PSO)× | |
|---|---|---|
| Campo | Optimización | Optimización |
| Familia≠ | Machine learning | Process / pipeline |
| Año de origen≠ | 2022 | 1995 |
| Autor original≠ | Xueying Shi | — |
| Tipo≠ | Nature-inspired metaheuristic algorithm | Population-based metaheuristic / swarm intelligence |
| Fuente seminal≠ | Shi, X., Sun, Y., Zhan, Z. H., Yuen, K. F., & Zhang, J. (2022). Jellyfish search optimizer: A new bio-inspired metaheuristic algorithm for solving optimization tasks. Neural Computing and Applications, 34(10), 7651-7673. link ↗ | Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948. DOI ↗ |
| Alias≠ | JSO | PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO) |
| Relacionados≠ | 3 | 6 |
| Resumen≠ | The Jellyfish Search Optimizer (JSO) is a biologically-inspired metaheuristic algorithm introduced by Shi et al. in 2022, based on the movement and foraging behavior of jellyfish in ocean environments. Jellyfish exhibit two distinct behaviors: passive drifting with ocean currents (exploration) and active swimming toward food sources (exploitation). JSO captures these behaviors to create an effective balance between global search and local refinement. | 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. |
| ScholarGateConjunto de datos ↗ |
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