পদ্ধতির তুলনা করুন

	অ্যাকুইলা অপ্টিমাইজার	Slime Mould Algorithm
ক্ষেত্র	অনুকূলকরণ	অনুকূলকরণ
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2021	2020
প্রবর্তক	Laith Abualigah	Shimin Li
ধরন	Nature-inspired metaheuristic algorithm	Nature-inspired metaheuristic algorithm
মৌলিক উৎস	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.
অপর নাম	AO	SMA
সম্পর্কিত	3	5
সারসংক্ষেপ	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.