เปรียบเทียบวิธี

	Differential Evolution	Firefly Algorithm	Genetic Algorithm
สาขาวิชา	การหาค่าเหมาะที่สุด	การหาค่าเหมาะที่สุด	การหาค่าเหมาะที่สุด
ตระกูล	Process / pipeline	Process / pipeline	Process / pipeline
ปีกำเนิด	1997	2008	1975
ผู้ริเริ่ม	Rainer Storn & Kenneth Price	Xin-She Yang	John Henry Holland
ประเภท	Population-based stochastic metaheuristic	Swarm intelligence metaheuristic	Population-based metaheuristic
แหล่งต้นตำรับ	Storn, R. & Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341–359.	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	DE algorithm, Diferansiyel Evrim (DE), DE optimization	FA, Firefly Optimization, Ateşböceği Algoritması (Firefly Algorithm)	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon
ที่เกี่ยวข้อง	5	5	5
สรุป	Differential Evolution (DE), introduced by Rainer Storn and Kenneth Price in 1997, is a population-based stochastic optimisation algorithm designed for continuous parameter spaces. It generates candidate solutions by combining vector differences between existing population members, making it a powerful and parameter-lean alternative to Genetic Algorithms and Particle Swarm Optimisation when the search landscape is non-convex, multimodal, or poorly suited to gradient-based methods.	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.