Compară metode

	Criteriul de Decizie Diferențială	Algoritm Genetic	Optimizatorul Lupilor Cenușii
Domeniu	Optimizare	Optimizare	Optimizare
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Anul apariției	1997	1975	2014
Autorul original	Rainer Storn & Kenneth Price	John Henry Holland	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis
Tip	Population-based stochastic metaheuristic	Population-based metaheuristic	Swarm-intelligence metaheuristic
Sursa seminală	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.	Holland, J.H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press.	Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.
Denumiri alternative	DE algorithm, Diferansiyel Evrim (DE), DE optimization	GA, evolutionary algorithm, Genetik Algoritma — Evrimsel Optimizasyon	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)
Înrudite	5	5	5
Rezumat	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.	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.	The Grey Wolf Optimizer (GWO) is a swarm-intelligence metaheuristic introduced by Mirjalili, Mirjalili, and Lewis in 2014 that models the social hierarchy and cooperative hunting behaviour of grey wolves. A population of candidate solutions is divided into four leadership ranks — alpha, beta, delta, and omega — and the three best solutions at each iteration guide the entire swarm toward increasingly better regions of the search space.