GA and ACO in hybrid approach for analog circuit performance optimization

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.