Numerical Analysis of The Differential Evolution Optimization Algorithm and Its Control  Parameters

BABAUOSMAIL, Idris; Hadj Daoud, ABDERRAHMAN

dc.contributor.author	BABAUOSMAIL, Idris
dc.contributor.author	Hadj Daoud, ABDERRAHMAN
dc.date.accessioned	2025-07-16T10:08:30Z
dc.date.available	2025-07-16T10:08:30Z
dc.date.issued	2024
dc.identifier.uri	https://di.univ-blida.dz/jspui/handle/123456789/40289
dc.description	4.333.1.366 ; 109 p	fr_FR
dc.description.abstract	This thesis aims to investigate the impact of control parameters and variants of the DE algorithm on its performance. By analyzing parameters like population size and mutation rates, alongside exploring various DE algorithm variants, the study aims to understand their combined influence on algorithm efficiency. Moreover, this research has the potential to be applied to various fields requiring optimization, including Maximum Power Point Tracking (MPPT).	fr_FR
dc.language.iso	en	fr_FR
dc.publisher	Blida1	fr_FR
dc.subject	Global Optimization, Meta-heuristic Algorithms (GA, ABC, PSO), Differential Evolution (DE), Control Parameters, DE Variants, Benchmark Functions, Convergence Quality, Convergence Speed, Execution Time.	fr_FR
dc.title	Numerical Analysis of The Differential Evolution Optimization Algorithm and Its Control Parameters	fr_FR