Optimization of CMOS Current Mirror Load-based Differential Amplifier using Hybrid Cuckoo Search and Particle Swarm Optimization Algorithm
Optimization of CMOS based analog circuits is becoming a practical solution for complex analog modules. There are number of evolutionary algorithms presented to optimize the analog circuits in different reported literatures. During optimization process, the optimal result may not be obtained with only individual algorithm or requires a more time. Therefore the hybrid algorithm of two or more algorithms is more effective method to obtain the goal. The Particle Swarm Optimization (PSO) algorithm is easy to implement and has good convergence speed. However, The Cuckoo Search (CS) algorithm has better skill to catch a global optimal result. A hybrid algorithm of CS and PSO (CSPSO) is developed to get the gains of the CS and PSO algorithms. In this work, CSPSO algorithm is tested to design the CMOS current mirror load-based differential amplifier (DA) with 180 nm CMOS technology parameters. The hybrid CSPSO algorithm is implemented with C language and interfaced with Ng-spice circuit simulator using script file. In this work, the CSPSO algorithm is used as a searching tool for transistor sizing and Ng-spice has been used as a fitness creator. The experimental simulation results show that the hybrid CSPSO algorithm outperforms with PSO and CS algorithms.
Cite this Article
Pankaj P. Prajapati, Mihir V. Shah, Optimization of CMOS Current Mirror Load-based Differential Amplifier using Hybrid Cuckoo Search and Particle Swarm Optimization Algorithm. Journal of Artificial Intelligence Research & Advances. 2018; 5(3): 71–78p.
Engelbrecht AP. Fundamentals of Computational Swarm Intelligence, Wiley, ISBN: 978-0-470-09191-3, 2005.
Fakhfakh M, Tlelo-Cuautle E, Castro-Lopez R. Analog/RF and Mixed-Signal Circuit Systematic Designb. Lecture Notes in Electrical Engineering. New York: Springer; 2013.
Holland J. H. Adaption in natural and artificial systems. University of Michigan Press, Ann Arbor, MIT Press, 1975.
Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by simulated annealing. Journal of Science. 1983; 220: 671–680p.
Glover F. Tabu search (part I). ORSA Journal on Computing. 1983; 1 (3): 190-206p.
Eiben AE, Smith JE. Introduction to evolutionary computing. Springer, ISBN 978-3-662-05094-1, 2007.
Kennedy J, Eberhart RC. Particle swarm optimization. In: Proceedings. IEEE International Conference on Neural Networks. Washington: IEEE; 1995.
Karaboga D. An idea based on Honey Bee swarm for numerical optimization. Technical Report-TR-06, Erciycs University, Engineering Faculty, Computer Engineering Dept., 2005.
Arunachalam V. Optimization using Differential Evolution. Water Resources Research Report no. 60. Facility for Intelligent Decision Support, Department of Civil and Environmental Engineering, London, Ontario, Canada, 2008.
Gupta H, Ghosh B. Analog Circuits Design using Ant Colony Optimization. IJECCT. 2012; 2: 9–21p.
Yang XS, Deb S. Cuckoo search via Levy flights. In: Nature & Biologically Inspired Computing (NaBIC 2009), India. USA: IEEE Publications; 2009.
Yang XS. Firefly algorithm, stochastic test functions and design optimization. International Journal of Bio-Inspired Computation. 2010; 2 (2): 78–84p.
Bayraktar Z, Komurcu M, Werner DH. Wind Driven Optimization (WDO): A novel Nature Inspired Optimization Algorithm and its application to electromagnetics. In: IEEE International Conference, Antennas and Propagation Society International Symposium (APSURSI), Toronto, 2010.
Yang XS. A new metaheuristic Bat-inspired Algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010),Springer Berlin, vol. 284. 2010. 65–74p.
Cheng M, Prayogo D. Symbiotic Organisms Search: A new metaheuristic optimization algorithm. Computers & Structures. 2014; 139: 98–112p.
Mirjalilia S, Mirjalilib SM, Lewisa A. Grey Wolf Optimizer. Advances in Engineering Software. 2014; 69: 46–61p.
Salimi. Stochastic Fractal Search: A Powerful Metaheuristic Algorithm. Knowledge-Based Systems. 2015; 75: 1–18p.
Hanan A, Akkar R, Firas R Mahdi. Grass Fibrous Root Optimization Algorithm. International Journal of Intelligent Systems and Applications (IJISA). 2017; 9 (6): 15–23p.
Prajapati PP, Shah MV. Performance Estimation of Differential Evolution, Particle Swarm Optimization and Cuckoo Search Algorithms. I.J. Intelligent Systems and Applications.
Vural RA, Yildirim T. Analog circuit sizing via swarm intelligence. International Journal of Electronics and Communications. 2012; 732–740p.
Civicioglu P, Besdok E. A conceptual comparison of the Cuckoo Search, Particle Swarm Optimization, Differential Evolution and Artificial Bee Colony algorithms, July 2011.
Fister I Jr, Fister D, Fister I. A comprehensive review of Cuckoo Search: variants and hybrids. International Journal of. Mathematical Modelling and Numerical Optimization. 2013; 4 (4): 387-409p.
Roy, Chaudhuri S. Cuckoo Search Algorithm using Lèvy flight: A Review. International Journal of Modern Education and Computer Science. 2013; 5 (12): 10–15p.
Allen PE, Holberg DR. CMOS Analog Circuit Design, 2nd edition. Oxford: Oxford University Press; 2013.
Prajapati PP, Shah MV. Automated sizing methodology of CMOS Miller Operational Transconductance Amplifier. In Soft Computing: Theories and Application, Advances in Intelligent systems and computing, Springer, Singapore, 2017, 584, 301-308p,
Thakker RA, Baghini MS, Patil MB. Low-power low-voltage analog circuit design using Hierarchical Particle Swarm Optimization. In: IEEE Computer society Internation conference on VLSI Design. 2009, 427-432p.
Prajapati PP, Shah MV. Optimization of CMOS based analog circuit using particle swarm optimization Algorithm. IPASJ International Journal of Electronics and Communication. 2015; 3 (8): 1–8p.
Sharma S, Prajapati PP. Design of Various Analog Circuits using Differential Evolutionary Algorithm. International Journal of Scientiﬁc Progress and Research, 2016; 23 (3): 159–165p.
- There are currently no refbacks.