Abstract
Ant Colony Optimization and Particle Swarm Optimization represent two widely used Swarm Intelligence (SI) optimization techniques. Information processing using Multiple-Valued Logic (MVL) is carried out using more than two discrete logic levels. In this paper, we compare two the SI-based algorithms in synthesizing MVL functions. A benchmark consisting of 50,000 randomly generated 2-variable 4-valued functions is used for assessing the performance of the algorithms using the benchmark. Simulation results show that the PSO outperforms the ACO technique in terms of the average number of product terms (PTs) needed. We also compare the results obtained using both ACO-MVL and PSO-MVL with those obtained using Espresso-MV logic minimizer. It is shown that on average, both of the SI-based techniques produced better results compared to those produced by Espresso-MV. We show that the SI-based techniques outperform the conventional direct-cover (DC) techniques in terms of the average number of product terms required.
Highlights
It is widely recognized by researchers as well as the chip industry that on-chip complex binary systems exhibit a number of curbs, such as large layout area for interconnections, limitations of data storage, increased power consumption, and limitation of available bandwidth
Particle swarm optimization (PSO) is inspired by the observation that birds fly in large groups and for long distances without collision
Concluding remarks This paper provides a review and comparison on the performance evaluation of the Ant Colony (ACO) and the Particle Swarm Optimization (PSO) heuristic techniques in the synthesis of Multiple-Valued Logic (MVL) functions
Summary
It is widely recognized by researchers as well as the chip industry that on-chip complex binary systems exhibit a number of curbs, such as large layout area for interconnections, limitations of data storage, increased power consumption, and limitation of available bandwidth. Publishers note: The publisher wishes to inform readers that the article “Swarm intelligence versus direct cover algorithms in synthesis of Multi-Valued Logic functions” was originally published by the previous publisher of Applied Computing and Informatics and the pagination of this article has been subsequently changed. We aim to provide a useful data analytics for researchers in the area of digital synthesis for high-radix (beyond binary) logic functions. The paper illustrates the adaptation of the discrete PSO algorithms in the area of MVL functional synthesis while showing the adopted processes used in the selection of the appropriate minterms and the appropriate implicants (product terms) to cover them.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
