Methoden vergelijken

	Rekenkundig Optimalisatie Algoritme	Differentiële Evolutie
Vakgebied	Optimalisatie	Optimalisatie
Familie	Machine learning	Process / pipeline
Jaar van ontstaan	2020	1997
Grondlegger	Laith Abualigah	Rainer Storn & Kenneth Price
Type	Mathematical metaheuristic algorithm	Population-based stochastic metaheuristic
Oorspronkelijke bron	Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A., & Gandomi, A. H. (2021). Arithmetic optimization algorithm: A new metaheuristic algorithm for solving optimization problems. Applied Mathematics and Computation, 392, 125450.	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.
Aliassen	AOA	DE algorithm, Diferansiyel Evrim (DE), DE optimization
Verwant	5	5
Samenvatting	The Arithmetic Optimization Algorithm (AOA) is a metaheuristic optimization approach introduced by Abualigah et al. in 2020 that leverages mathematical operators (multiplication, division, addition, subtraction) as the inspiration for search strategies. Unlike nature-inspired algorithms, AOA uses the inherent properties of arithmetic operations to balance exploration and exploitation, making it particularly effective for mathematical optimization problems.	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.