Abstract
The simplex particle swarm optimization (Simplex-PSO) is a swarm intelligent based evolutionary computation method. Simplex-PSO is the hybridization of Nedler–Mead simplex method and particle swarm optimization (PSO) without the velocity term. The Simplex-PSO has fast optimizing capability and high computational precision for high-dimensionality functions. In this paper, Simplex-PSO is employed for selection of optimal discrete component values such as resistors and capacitors for fourth order Butterworth low pass analog active filter and second order State Variable low pass analog active filter, respectively. Simplex-PSO performs the dual task of efficiently selecting the component values as well as minimizing the total design errors of low pass analog active filters. The component values of the filters are selected in such a way so that they become E12/E24/E96 series compatible. The simulation results prove that Simplex-PSO efficiently minimizes the total design error to a greater extent in comparison with previously reported optimization techniques.
References
Sanchez-Sinencio E, Silva-Martinez J (2000) CMOS transconductance amplifiers, architectures and active filters: a tutorial. IEEE Proc Circuits Devices Syst 147(1):3–12
Zebulum RS, Pacheco MA, Vellasco M (1999) Artificial evolution of active filters: a case study. In: Proceedings of the 1st NASA DoD workshop on evolvable hardware, pp 66–75
Xu H, Ding Y (2009) Optimizing method for analog circuit design using adaptive immune genetic algorithm. In: Proceedings of the international conference on frontier of computer science and technology, pp 359–63
Jiang M, Yang Z, Gan Z (2007) Optimal components selection for analog active filters using clonel selection algorithm. Proc ICICI LNCS 4681:950–959
Vural RA, Yildirim T (2010) Component value selection for analog active filter using particle swarm optimization. In: Proceedings of the 2nd IEEE international conference on computer and automation engineering, vol 1, pp 25–28
Vural RA, Yildirim T, Kadioglu T, Basargan A (2012) Performance evaluation of evolutionary algorithms for optimal filter design. IEEE Trans Evol Comput 16(1):135–147
Vural RA, Bozkurt U, Yildirim T (2013) Analog active filter component selection with nature inspired meta heuristics. Int J Electron Commun (AEÜ) 67(3):197–205
Vural RA, Yildirim T (2010) State variable filter design using particle swarm optimization. In: Proceedings of the XIth international workshop on symbolic and numerical methods, modeling and applications to circuit design, pp 1–4
Kalinli A (2004) Optimal circuit design using immune algorithm. Proc ICARIS LNCS 3239:42–52
Kalinli A (2006) Component value selection for active filters using parallel Tabu search algorithm. Int J Electron Commun (AEÜ) 60(1):85–92
Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942–1948
Eberhart R, Shi Y (1998) Comparison between genetic algorithm and particle swarm optimization. In: Evolutionary programming-VII, Springer, New York, pp 611–616
Luitel B, Venayagamoorthy GK (2010) Particle swarm optimization with quantum infusion for system identification. Eng Appl Artif Intell 23:635–649
Yu X, Liu J, Li H (2009) An adaptive inertia weight particle swarm optimization algorithm for IIR digital filter. In: IEEE international conference on artificial and computational intelligence, pp 114–118
Fang W, Sun J, Xu W (2009) A new mutated quantum behaved particle swarm optimizer for digital IIR filter design. EURASIP J Adv Signal Process 2009 (article ID 367465):1–7
Kennedy J (2003) Bare bones particle swarms. In: IEEE swarm intelligence symposium, USA, pp 80–87, 24–26 April 2003
Hong-feng X, Guan-Zheng T (2010) A novel particle swarm optimizer without velocity: simplex-PSO. J Central South Univ 17(2):349–356
Wang H, Zhi-shu L (2007) A simpler and more effective particle swarm optimization. J Softw 18(4):861–868
Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7(2):308–313
Schaumann R, Valkenburg MV (2001) Design of analog filters. Oxford University Press, New York
Mohan P (2010) Sensitivity analysis of third and fourth-order filters. Circuits Syst Signal Process 29(5):999–1005
Gomez G, Cuautle ET, de la Fraga LG (2013) Richardson extrapolation-based sensitivity analysis in the multi-objective optimization of analog circuits. Appl Math Comput 222:167–176
Zhang J, Chau K-W (2009) Multilayer ensemble pruning via novel multi-sub-swarm particle swarm optimization. J Univ Comput Sci 15(4):840–858
Chau KW (2007) Application of a PSO-based neural network in analysis of outcomes of construction claims. Autom Constr 16(5):642–646
Ali Rıza Yıldız (2009) A novel particle swarm optimization approach for product design and manufacturing. Int J Adv Manuf Technol 40(5–6):617–628
Yildiz AR (2012) A new hybrid particle swarm optimization approach for structural design optimization in the automotive industry 226(D10):1340–1351
Yildiz AR, Solanki KN (2012) Multi-objective optimization of vehicle crashworthiness using a new particle swarm based approach. Int J Adv Manuf Technol 59(1–4):367–376
Fakhfakh M, Cooren Y, Sallem A, Loulou M, Siarry P (2010) Analog circuit design optimization through the particle swarm optimization technique. Analog Integr Circuits Signal Process 63(1):71–82
Rana S, Jasola S, Kumar R (2013) A boundary restricted adaptive particle swarm optimization for data clustering. Int J Mach Learn Cybern 4(4):391–400
Wang X-Z, He Y-L, Dong L-C, Zhao H-Y (2011) Particle swarm optimization for determining fuzzy measures from data. Inf Sci 181(19):4230–4252
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
De, B.P., Kar, R., Mandal, D. et al. Optimal selection of components value for analog active filter design using simplex particle swarm optimization. Int. J. Mach. Learn. & Cyber. 6, 621–636 (2015). https://doi.org/10.1007/s13042-014-0299-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13042-014-0299-0