Benhala, B. and Ahaitouf, A. (2014) GA and ACO in hybrid approach for analog circuit performance optimization. In: UNSPECIFIED.

Full text not available from this repository.

Abstract

During optimization problems, the optimal solution is sometimes unachieved with only one algorithm ore needs a long time. Hence the hybridation of two or more technics to reach the aim seems to be a more efficient technique. In this paper, the genetic algorithm (GA) and the ant colony optimization one (ACO) are used in a hybridation way to explore the search space and exploit the best solutions. The obtained hybrid algorithms (GAACO) and (ACOGA) are used for the sizing of a CMOS second generation current conveyor (CCII) and an operational amplifier (Op-Amp). The performances of the proposed algorithms will be highlighted in terms of computing time, convergence rate and the optimum quality. © 2014 IEEE.

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords:	Analog circuits; Artificial intelligence; Conveyors; Genetic algorithms; Operational amplifiers; Timing circuits, Analog design; Circuit performance; Current conveyors; Hybridation; Operational amplifier (op amp); Optimal solutions; Optimization problems; Second generation current conveyors, Ant colony optimization
Subjects:	Computer Science
Divisions:	SCIENTIFIC PRODUCTION > Computer Science
Depositing User:	Administrateur Eprints Administrateur Eprints
Last Modified:	31 Jan 2020 15:46
URI:	http://eprints.umi.ac.ma/id/eprint/2671

Actions (login required)

View Item