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

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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

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