Use of Particle Swarm Optimization to Design Combinational Logic Circuits
This paper presents a proposal based on binary particle swarm optimization to design combinational logic circuits at the gatelevel. The proposed algorithm is validated using several examples from the literature, and is compared against a genetic algorithm (with integer representation), and against human designers who used traditional circuit design aids (e.g., Karnaugh Maps). Results indicate that particle swarm optimization may be a viable alternative to design combinational circuits at the gate-level.
KeywordsGenetic Algorithm Particle Swarm Optimization Truth Table Human Designer Arithmetic Circuit
Unable to display preview. Download preview PDF.
- 1.Carlos A. Coello Coello, Alan D. Christiansen, and Arturo Hernández Aguirre. Automated Design of Combinational Logic Circuits using Genetic Algorithms. In D. G. Smith, N. C. Steele, and R. F. Albrecht, editors, Proceedings of the International Conference on Artificial Neural Nets and Genetic Algorithms, pages 335–338. Springer-Verlag, University of East Anglia, England, April 1997.Google Scholar
- 2.Carlos A. Coello Coello, Alan D. Christiansen, and Arturo Hernández Aguirre. Use of Evolutionary Techniques to Automate the Design of Combinational Circuits. International Journal of Smart Engineering System Design, 2(4):299–314, June 2000.Google Scholar
- 3.Carlos A. Coello Coello, Arturo Hernández Aguirre, and Bill P. Buckles. Evolutionary Multiobjective Design of Combinational Logic Circuits. In Jason Lohn, Adrian Stoica, Didier Keymeulen, and Silvano Colombano, editors, Proceedings of the Second NASA/DoD Workshop on Evolvable Hardware, pages 161–170, Los Alamitos, California, July 2000. IEEE Computer Society.Google Scholar
- 4.Russell C. Eberhart and Yuhui Shi. Comparison between Genetic Algorithms and Particle Swarm Optimization. In V. W. Porto, N. Saravanan, D. Waagen, and A. E. Eibe, editors, Proceedings of the Seventh Annual Conference on Evolutionary Programming, pages 611–619. Springer-Verlag, March 1998.Google Scholar
- 5.Tatiana Kalganova and Julian Miller. Evolving more efficient digital circuits by allowing circuit layout and multi-objective fitness. In Adrian Stoica, Didier Keymeulen, and Jason Lohn, editors, Proceedings of the First NASA/DoD Workshop on Evolvable Hardware, pages 54–63, Los Alamitos, California, 1999. IEEE Computer Society Press.Google Scholar
- 6.James Kennedy and Russell C. Eberhart. Particle Swarm Optimization. In Proceedings of the 1995 IEEE International Conference on Neural Networks, pages 1942–1948, Piscataway, New Jersey, 1995. IEEE Service Center.Google Scholar
- 7.James Kennedy and Russell C. Eberhart. A Discrete Binary Version of the Particle Swarm Algorithm. In Proceedings of the 1997 IEEE Conference on Systems, Man, and Cybernetics, pages 4104–4109, Piscataway, New Jersey, 1997. IEEE Service Center.Google Scholar
- 8.James Kennedy and Russell C. Eberhart. Swarm Intelligence. Morgan Kaufmann Publishers, San Francisco, California, 2001.Google Scholar
- 9.Sushil J. Louis. Genetic Algorithms as a Computational Tool for Design. PhD thesis, Department of Computer Science, Indiana University, August 1993.Google Scholar
- 10.Sushil J. Louis and Gregory J. Rawlins. Using Genetic Algorithms to Design Structures. Technical Report 326, Computer Science Department, Indiana University, Bloomington, Indiana, February 1991.Google Scholar
- 11.J. F. Miller, P. Thomson, and T. Fogarty. Designing Electronic Circuits Using Evolutionary Algorithms. Arithmetic Circuits: A Case Study. In D. Quagliarella, J. Périaux, C. Poloni, and G. Winter, editors, Genetic Algorithms and Evolution Strategy in Engineering and Computer Science, pages 105–131. Morgan Kaufmann, Chichester, England, 1998.Google Scholar
- 13.Tsutomu Sasao, editor. Logic Synthesis and Optimization. Kluwer Academic Press, 1993.Google Scholar