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BY-NC-ND 3.0 license Open Access Published by De Gruyter Open Access July 7, 2015

Optimal high speed CMOS inverter design using craziness based Particle Swarm Optimization Algorithm

  • Bishnu P. De , Rajib Kar , Durbadal Mandal and Sakti P. Ghoshal
From the journal Open Engineering


The inverter is the most fundamental logic gate that performs a Boolean operation on a single input variable. In this paper, an optimal design of CMOS inverter using an improved version of particle swarm optimization technique called Craziness based Particle Swarm Optimization (CRPSO) is proposed. CRPSO is very simple in concept, easy to implement and computationally efficient algorithm with two main advantages: it has fast, nearglobal convergence, and it uses nearly robust control parameters. The performance of PSO depends on its control parameters and may be influenced by premature convergence and stagnation problems. To overcome these problems the PSO algorithm has been modiffed to CRPSO in this paper and is used for CMOS inverter design. In birds’ flocking or ffsh schooling, a bird or a ffsh often changes direction suddenly. In the proposed technique, the sudden change of velocity is modelled by a direction reversal factor associated with the previous velocity and a "craziness" velocity factor associated with another direction reversal factor. The second condition is introduced depending on a predeffned craziness probability to maintain the diversity of particles. The performance of CRPSO is compared with real code.gnetic algorithm (RGA), and conventional PSO reported in the recent literature. CRPSO based design results are also compared with the PSPICE based results. The simulation results show that the CRPSO is superior to the other algorithms for the examples considered and can be efficiently used for the CMOS inverter design.


[1] Ma Q., Cowan C.F.N., Genetic algorithms applied to the adaptation of IIR filters, Signal Process., 1996, 48, 155–163. 10.1016/0165-1684(95)00131-XSearch in Google Scholar

[2] Luitel B., Venayagamoorthy G.K., Particle swarm optimization with quantum infusion for system identification, Eng. Appl. Artif. Intel., 2010, 23, 635–649. 10.1016/j.engappai.2010.01.022Search in Google Scholar

[3] Kar R.,Mandal D.,Mondal S., Ghoshal S.P., Craziness based Particle Swarm Optimization Algorithm for FIR Band Stop Filter Design, Swarm Evol. Comput., 2012, 7, 58–64. 10.1016/j.swevo.2012.05.002Search in Google Scholar

[4] Hussain Z.M., Sadik A.Z., O’Shea P., Digital Signal Process.-An Introduction with MATLAB Applications, Springer-Verlag, 2011. Search in Google Scholar

[5] Fang W., Sun J., Xu W., A new mutated quantum behaved particle swarm optimizer for digital IIR filter design, EURASIP J. Adv. Signal. Process., DOI:10.1155/2009/367465 10.1155/2009/367465Search in Google Scholar

[6] Krusienski D.J., Jenkins W.K., Adaptive filtering via particle swarm optimization, Proceedings of 37th Asilomar Conference on Signals, Systems and Computers (9-12 November 2003), 2003, 571–575. Search in Google Scholar

[7] Mandal S., Ghoshal S.P., Kar R., Mandal D., Design of Optimal Linear Phase FIR High Pass Filter using Craziness based Particle Swarm Optimization Technique, Journal of King Saud University-Computer and Information Sciences, 2012, 24, 83– 92. 10.1016/j.jksuci.2011.10.007Search in Google Scholar

[8] Saha S.K., Kar R., Mandal D., Ghoshal S.P., IIR filter design with craziness based particle swarm optimization technique, World. Acad. Sci. Eng. Technol., 2011, 5, 1052–1059. 10.1109/SHUSER.2012.6268873Search in Google Scholar

[9] Holland J.H., Adaptation in Natural and Artificial Systems, MIT Press Cambridge, MA, USA, 1975. Search in Google Scholar

[10] Mastorakis N.E., Gonos I.F., Swamy M.N.S., Design of Two Dimensional Recursive Filters Using Genetic Algorithms, IEEE Trans. Circuits Syst. I, Fundam. Theory Appl., 2003, 50, 634– 639. 10.1109/TCSI.2003.811019Search in Google Scholar

[11] Lu H.C., Tzeng S.T., Design of arbitrary FIR log filters by genetic algorithm approach, Signal Process., 2000, 80, 497–505. 10.1016/S0165-1684(99)00146-2Search in Google Scholar

[12] Das A., Vemuri R., An Automated Passive Analog Circuit Synthesis Framework using Genetic Algorithms, IEEE Computer Society Annual Symposiumon VLSI 7 (9-11March 2007), 2007, 145–152. Search in Google Scholar

[13] Gill S.S., Chandel R., Chandel A., Comparative study of Ant Colony and Genetic Algorithms for VLSI circuit partitioning, World. Acad. Sci. Eng. Technol., 2009, 28, 890–894. Search in Google Scholar

[14] Tang M., Lau R.Y.K., A Parallel Genetic Algorithm for Floor plan Area Optimization, 7th International Conference on Intelligent Systems Design and Application (20-24 October 2007, Rio de Janeiro, Brazil), 2007, 801–806. 10.1109/ISDA.2007.47Search in Google Scholar

[15] Kennedy J., Eberhart R., Particle swarm optimization, Proceedings of IEEE International Conference on Neural Networks (Nov/Dec 1995, Perth, WA), 1995, 1942–1948. Search in Google Scholar

[16] Eberhart R., Shi Y., Comparison between genetic algorithm and particle swarm optimization, Evolutionary Programming- VII, 1998, 1447, 611–616. 10.1007/BFb0040812Search in Google Scholar

[17] Chen S., Luk B.L., Digital IIR filter design using particle swarm optimization, Int. J. Modelling, Identification and Control, 2010, 9, 327–335. 10.1504/IJMIC.2010.033208Search in Google Scholar

[18] Gudise V.G., Venayagamoorthy G.K., FPGA placement and routing using particle swarm optimization, IEEE Computer Society Annual Symposium on VLSI (19-20 February 2004), 2004, 307– 308. Search in Google Scholar

[19] Lai C., A novel image segmentation approach based on particle swarm optimization, IEICE Trans. Fundamentals, 2006, E89-A, 324–327. 10.1093/ietfec/e89-a.1.324Search in Google Scholar

[20] Ulker S., Particle swarm optimization applications to microwave circuits, Microw. Opt. Technol. Lett., 2008, 50, 1333–1336. 10.1002/mop.23369Search in Google Scholar

[21] Vural R. A., Yildirim T., Analog circuit sizing via swarm intelligence, Int. J. Electron. Commun. (AEÜ), 2012, 66, 732– 740 10.1016/j.aeue.2012.01.003Search in Google Scholar

[22] Ling S.H., Iu H.H.C., Leung F.H.F., Chan K.Y., Improved hybrid particle swarm optimized wavelet neural network for modelling the development of fluid dispensing for electronic packaging, IEEE Trans. Ind. Electron., 2008, 55, 3447–3460. 10.1109/TIE.2008.922599Search in Google Scholar

[23] Biswal B., Dash P.K., Panigrahi B.K., Power quality disturbance classification using fuzzy c-means algorithm and adaptive particle swarm optimization, IEEE Trans. Ind. Electron., 2009, 56, 212–220. 10.1109/TIE.2008.928111Search in Google Scholar

[24] Saha S.K., Kar R., Mandal D., Ghoshal S.P., An Efficient Craziness Based Particle Swarm Optimization Technique for Optimal IIR Filter Design, Transactions on Computational Science XXI, 2013, 8160, 230–252. 10.1007/978-3-642-45318-2_10Search in Google Scholar

[25] Vural R.A., DerO., Yildirim T., Investigation of particle swarm optimization for switching characterization of inverter design, Expert. Syst. Appl., 2011, 38, 5696–5703. 10.1016/j.eswa.2010.10.064Search in Google Scholar

[26] Vural R.A., DerO., Yildirim T., Particle swarm optimization based inverter design considering transient performance, Digital Signal Process., 2010, 20, 1215–1220. 10.1016/j.dsp.2009.10.022Search in Google Scholar

[27] Mukhopadhyay J., Pandit S., Modeling and Design of a Nano Scale CMOS Inverter for Symmetric Switching Characteristics, VLSI Design, DOI:10.1155/2012/505983. 10.1155/2012/505983Search in Google Scholar

[28] Karaboga N., Digital IIR filter design using differential evolution algorithm, EURASIP J. Adv. Signal. Process., DOI:10.1155/ASP.2005.1269. 10.1155/ASP.2005.1269Search in Google Scholar

[29] Karaboga N., A new design method based on artificial bee colony algorithm for digital IIR filters, J. Franklin Inst., 2009, 346, 328–348. 10.1016/j.jfranklin.2008.11.003Search in Google Scholar

[30] Hardel R.G., Mandal D., Ghoshal S.P., Kar R., Minimization of side lobe of optimized uniformly spaced and non-uniform excited time modulated linear antenna arrays using genetic algorithm, Swarm, Evolutionary, and Memetic Computing, Lecture Notes in Computer Science, 2012, 7677, 451–458. 10.1007/978-3-642-35380-2_53Search in Google Scholar

[31] Mandal D., Ghoshal S. P., Bhattacharjee A. K., Radiation Pattern Optimization for Concentric Circular Antenna ArrayWith Central Element Feeding Using Craziness Based Particle Swarm Optimization, Int. J. RF Microw. C. E. , 2010, 20, 577–586. 10.1002/mmce.20467Search in Google Scholar

[32] De B.P., Kar R., Mandal D., Ghoshal S.P., Optimal analog active filter design using craziness-based particle swarm optimization algorithm, Int. J. Numer. Model., DOI:10.1002/jnm.2040 10.1002/jnm.2040Search in Google Scholar

[33] Walpole R.E., Myers R.H., Probability and statistics for engineers and scientists, Macmillan Publishing Co., Inc., New York, 1978. 10.2307/2530629Search in Google Scholar

[34] DeMassa T.A., Ciccone Z., Digital Integrated Circuits, JohnWiley & Sons, New York, 1996. Search in Google Scholar

[35] Kang S.M., Leblebici Y., CMOS Digital Integrated Circuits Analysis and Design, TMH, India, 2003. Search in Google Scholar

Received: 2014-7-30
Accepted: 2015-4-20
Published Online: 2015-7-7

©2015 B.P. De et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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