เปรียบเทียบวิธี

	Firefly Algorithm	Genetic Algorithm
สาขาวิชา	การหาค่าเหมาะที่สุด	การหาค่าเหมาะที่สุด
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2008	1975
ผู้ริเริ่ม	Xin-She Yang	John Henry Holland
ประเภท	Swarm intelligence metaheuristic	Population-based metaheuristic
แหล่งต้นตำรับ	Yang, X.S. (2010). Firefly Algorithm, Stochastic Test Functions and Design Optimisation. International Journal of Bio-Inspired Computation, 2(2), 78-84.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.
ชื่อเรียกอื่น	FA, Firefly Optimization, Ateşböceği Algoritması (Firefly Algorithm)	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
ที่เกี่ยวข้อง	5	5
สรุป	The Firefly Algorithm (FA), introduced by Xin-She Yang in 2008 and formally published in 2010, is a nature-inspired swarm metaheuristic that models the bioluminescent attraction behaviour of fireflies. Each candidate solution is a firefly whose brightness represents its objective-function value; dimmer fireflies move toward brighter ones with an attraction force that decays with distance, driving the swarm toward optima without gradient information.	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.