เปรียบเทียบวิธี

	อัลกอริทึม Slime Mould	Genetic Algorithm
สาขาวิชา	การหาค่าเหมาะที่สุด	การหาค่าเหมาะที่สุด
ตระกูล	Machine learning	Process / pipeline
ปีกำเนิด	2020	1975
ผู้ริเริ่ม	Shimin Li	John Henry Holland
ประเภท	Nature-inspired metaheuristic algorithm	Population-based metaheuristic
แหล่งต้นตำรับ	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.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
ชื่อเรียกอื่น	SMA	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
ที่เกี่ยวข้อง	5	5
สรุป	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.	A genetic algorithm (GA) is a population-based metaheuristic optimization method introduced by John Henry Holland (1975) that mimics the principles of natural selection. It maintains a population of candidate solutions and iteratively improves them through selection, crossover, and mutation operators, making it especially powerful on discontinuous, non-convex, and multi-modal search spaces where classical gradient-based methods fail.