Sammenlign metoder

	Differential Evolution	Grey Wolf Optimizer
Fagområde	Optimering	Optimering
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	1997	2014
Ophavsperson	Rainer Storn & Kenneth Price	Seyedali Mirjalili, Seyed Mohammad Mirjalili, Andrew Lewis
Type	Population-based stochastic metaheuristic	Swarm-intelligence metaheuristic
Oprindelig kilde	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.	Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances in Engineering Software, 69, 46-61.
Aliasser	DE algorithm, Diferansiyel Evrim (DE), DE optimization	GWO, Gri Kurt Optimizasyonu, Gri Kurt Optimizasyonu (GWO)
Relaterede	5	5
Resumé	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 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.