Comparar métodos

	Jellyfish Search Optimizer	Aquila Optimizer	Algoritmo do Bolor Limoso
Área	Otimização	Otimização	Otimização
Família	Machine learning	Machine learning	Machine learning
Ano de origem	2022	2021	2020
Autor original	Xueying Shi	Laith Abualigah	Shimin Li
Tipo	Nature-inspired metaheuristic algorithm	Nature-inspired metaheuristic algorithm	Nature-inspired metaheuristic algorithm
Fonte 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.	Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Aquila optimizer: A novel meta-heuristic optimization algorithm. Computers and Industrial Engineering, 157, 107250.	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.
Outros nomes	JSO	AO	SMA
Relacionados	3	3	5
Resumo	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.	The Aquila Optimizer (AO) is a nature-inspired metaheuristic algorithm presented by Abualigah et al. in 2021, modeled after the hunting behavior and sensory abilities of golden eagles (aquila chrysaetos). The algorithm captures the exploration and exploitation phases of eagle hunting, including high-altitude soaring, exploration with high-precision vision, and rapid diving attacks. AO is designed to solve both constrained and unconstrained 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.