Functional synthesis using discrete particle swarm optimization

Application of multi-valued (non-binary) digital signals can provide considerable relief for a number of problems faced in binary systems, such as increased functional density and interconnection wirings. Heuristics have been used to synthesize multiple-valued logic (MVL) functions using near optimal number of product terms. In this paper, we explore the use of particle swarm optimization algorithm for synthesis of MV functions. The proposed approach was tested against 50000 randomly generated 2-variable 4-valued functions. The results show that the proposed algorithm outperforms other deterministic and ant colony based approaches in terms of the average number of product terms needed to synthesize a given MVL function.