Logic Optimization of QCA Circuits using Ant Colony Optimization

Implementation of logic circuits using Quantum-dot Cellular Automata (QCA) technology due to the proper features, e.g. high device density, fast switching time and extremely low power, is an intense research area. Boolean primitives in QCA circuits are the majority gate and inverter. Recently, evolutionary algorithms, especially Genetic Algorithm (GA), have been used for implementing a given Boolean function with the minimum number of required primitives. In this paper, a novel method for optimal implementation of three variable Boolean functions by using Ant Colony Optimization (ACO) algorithm is presented. Simulation results demonstrate that the proposed method outperforms GA-based methods in the average number of required gates and levels for implementing three variable Boolean functions.