Synthesis of MVL Functions - Part II: The Ant Colony Optimization Approach

Abstract

In this paper, an ant colony optimization (ACO) based algorithm for synthesis of Multiple-Valued Logic (MVL) functions is proposed. The algorithm is tested using 50000 randomly generated 2-variable 4-valued functions. The proposed approach was compared to the existing direct cover techniques. The results obtained show that the proposed algorithm outperforms other approaches in terms of the average number of product terms required to realize a given MVL function.