เปรียบเทียบวิธี

	Jellyfish Search Optimizer	อัลกอริทึม Slime Mould
สาขาวิชา	การหาค่าเหมาะที่สุด	การหาค่าเหมาะที่สุด
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2022	2020
ผู้ริเริ่ม	Xueying Shi	Shimin Li
ประเภท	Nature-inspired metaheuristic algorithm	Nature-inspired metaheuristic algorithm
แหล่งต้นตำรับ	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.	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.
ชื่อเรียกอื่น	JSO	SMA
ที่เกี่ยวข้อง	3	5
สรุป	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 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.