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CMOS analogue amplifier circuits optimisation using hybrid backtracking search algorithm with differential evolution

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2016年第4期28卷 719-749页

作者： Mallick, S. Kar, R. Mandal, D. Ghoshal, S. P. NIT Durgapur Dept Elect & Commun Engn Durgapur W Bengal India NIT Durgapur Dept Elect Engn Durgapur W Bengal India

This paper proposes a novel hybrid optimisation algorithm which combines the recently proposed evolutionary algorithm Backtracking Search Algorithm (BSA) with another widely accepted evolutionary algorithm, namely, Differential Evolution (DE). The proposed algorithm called BSA-DE is employed for the optimal designs of two commonly used analogue circuits, namely Complementary Metal Oxide Semiconductor (CMOS) differential amplifier circuit with current mirror load and CMOS two-stage operational amplifier (op-amp) circuit. BSA has a simple structure that is effective, fast and capable of solving multimodal problems. DE is a stochastic, population-based heuristic approach, having the capability to solve global optimisation problems. In this paper, the transistors' sizes are optimised using the proposed BSA-DE to minimise the areas occupied by the circuits and to improve the performances of the circuits. The simulation results justify the superiority of BSA-DE in global convergence properties and fine tuning ability, and prove it to be a promising candidate for the optimal design of the analogue CMOS amplifier circuits. The simulation results obtained for both the amplifier circuits prove the effectiveness of the proposed BSA-DE-based approach over DE, harmony search (HS), artificial bee colony (ABC) and PSO in terms of convergence speed, design specifications and design parameters of the optimal design of the analogue CMOS amplifier circuits. It is shown that BSADE- based design technique for each amplifier circuit yields the least MOS transistor area, and each designed circuit is shown to have the best performance parameters such as gain, power dissipation, etc., as compared with those of other recently reported literature.

关键词： CMOS circuit sizing two-stage operational amplifier differential amplifier backtracking search algorithm differential evolution evolutionary optimisation techniques

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A new design method using opposition-based BAT algorithm for IIR system identification problem

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2013年第2期5卷 99-132页

作者： Saha, Suman Kumar Kar, Rajib Mandal, Durbadal Ghoshal, Sakti Prasad Mukherjee, Vivekananda Natl Inst Technol Durgapur Elect & Commun Engn Dept Durgapur 713209 W Bengal India Indian Sch Mines Dept Elect Engn Dhanbad 826004 Jharkhand India

BAT algorithm (BA) is a meta-heuristic algorithm, based on the echolocation behaviour of bats. In this paper, optimal set of filter coefficients is searched by the modified optimisation methodology called opposition-based BAT algorithm (OBA) for infinite impulse response (IIR) system identification problem. Opposition based numbering concept is embedded into the primary foundation of BA metaphorically to enhance the convergence speed and performance for finding better near-global optimal solution. Detailed and balanced search in multidimensional problem space is accomplished with judiciously chosen control parameters of OBA technique. When tested against standard benchmark examples, for same and reduced order models, the simulation results establish the OBA as a more competent candidate to other evolutionary algorithms as real coded genetic algorithm (RGA), differential evolution (DE) and particle swarm optimisation (PSO) in terms of accuracy and convergence speed.

关键词： IIR adaptive filter evolutionary optimisation techniques mean square error MSE coefficient convergence

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SEOA-based optimal design of analogue CMOS amplifier circuits

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2017年第4期9卷 211-225页

作者： Mallick, Soumen Kar, Rajib Ghoshal, Sakti Prasad Mandal, Durbadal NIT Durgapur Dept Elect & Commun Engn Durgapur India NIT Durgapur Dept Elect Engn Durgapur India

This paper presents social emotional optimisation algorithm (SEOA) based optimal designs of two analogue CMOS amplifier circuits. In SEOA, human behaviour is organised for attaining superior position in society. The person with the topmost position in society provides the optimal solution in multi-dimensional search space in this virtual world. To earn the supreme position in society through the assistance and rivalry with others not only results in better exploration and exploitation of problem space but also ensures faster convergence to optimal solution. The transistors' sizes are optimised to minimise the total MOS occupied area with better performance parameters of the circuits. The simulation results prove that the efficiency of the proposed SEOA-based approach over DE, harmony search (HS), artificial bee colony (ABC) and PSO in terms of convergence speed, specifications of design parameters and yields the least MOS area with improved gain and lower power dissipation.

关键词： CMOS circuit sizing two-stage op-amp differential amplifier social emotional optimisation algorithm SEOA evolutionary optimisation techniques low power design

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High-performance Brillouin distributed temperature sensor using Fourier wavelet regularised deconvolution algorithm

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IET OPTOELECTRONICS 2014年第6期8卷 203-209页

作者： Pradhan, Himansu Shekhar Sahu, Prasant Kumar Indian Inst Technol Bhubaneswar Sch Elect Sci Bhubaneswar 751013 Odisha India

In this study, the authors presented a distributed optical fibre temperature sensor whose performance is improved using the optimisation techniques and Fourier wavelet regularised deconvolution (ForWaRD) method. As the input power launched into the sensing fibre is critical, the authors have considered both conventional optimisation technique and evolutionary optimisation techniques namely: genetic algorithm, differential evolution algorithm and particle swarm optimisation algorithm to increase the stimulated Brillouin scattering (SBS) threshold power. Using the optimised value of the parameters and employing evolutionary computing techniques, the proposed 50 km long temperature sensing system offers a 4.7 dB improvement in SBS threshold power over the design of experiment based system. It is being verified that power of the backscattered signals approximately are the convolution of the input pulsed power and corresponding backscatter optical fibre impulse responses. The Fourier wavelet regularised deconvolution (ForWaRD) method is employed to improve the spatial resolution of the proposed sensing system without reducing the pulse width of the input pulse. Employing ForWaRD technique a 10 m better spatial resolution observed as compared with the Fourier deconvolution technique. The proposed 50 km long temperature sensing system exhibits a temperature resolution of 1.85 K because of suppression of SBS threshold and noise reduction.

关键词： fibre optic sensors temperature sensors stimulated Brillouin scattering Fourier transform optics wavelet transforms deconvolution genetic algorithms particle swarm optimisation distributed sensors optical information processing signal denoising high-performance Brillouin distributed temperature sensor Fourier wavelet regularised deconvolution algorithm optical fibre temperature sensor ForWaRD method evolutionary optimisation techniques genetic algorithm differential evolution algorithm particle swarm optimisation algorithm stimulated Brillouin scattering SBS threshold power input pulsed power convolution backscatter optical fibre impulse responses spatial resolution SBS threshold suppression noise reduction

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Optimal design of second generation current conveyor using craziness-based particle swarm optimisation

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2022年第2期19卷 87-96页

作者： Mallick, Soumen Kar, Rajib Mandal, Durbadal Dr BC Roy Engn Coll Dept Informat Technol Durgapur W Bengal India NIT Durgapur Dept Elect & Commun Engn Durgapur W Bengal India

In this paper, a population-based meta-heuristic search algorithm called craziness-based particle swarm optimisation (CRPSO) has been employed for the optimal design of positive second-generation current conveyor based on a trans-linear loop (CCII+). CRPSO accepts several random variables for encompassing improved and quicker search and utilisation in multidimensional search space. The main goal of this work is to optimally size the CCII+ building block to attain the suitable aspect ratios of the MOS transistors. To enhance the performance of a CCII+ design, it needs to increase the higher cut-off frequency of the current signal (f(CI)) as well as to decrease the input X-port parasitic resistance (R-X). Accordingly, the optimisation problem is developed as a bi-objective problem of minimisation to obtain the least value of R-X and the superior value of f(CI). The current conveyor is simulated and validated using UMC 180 nm CMOS technology with a power supply of +/- 2.5 V in Cadence Virtuoso XL Design Environment.

关键词： CMOS circuit sizing second generation current conveyor trans-linear loop CRPSO evolutionary optimisation techniques X-port parasitic resistance higher cut-off frequency

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A novel social emotional optimisation algorithm for IIR system identification problem

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2014年第1期22卷 80-112页

作者： Upadhyay, Prashant Kar, Rajib Mandal, Durbadal Ghoshal, Sakti Prasad Natl Inst Technol Dept Elect & Commun Engn MG Ave Durgapur 713029 India Natl Inst Technol Dept Elect Engn Durgapur 713029 India

In this paper an evolutionary optimisation methodology based on social emotional optimisation algorithm (SEOA) is applied to the infinite impulse response (IIR) system identification problem. In SEOA methodology, behaviour of human beings for achieving higher social status in society is structured. In this virtual world, the individual with the highest rank in society gives the optimal solution in multidimensional search space. Earning the highest social status by means of cooperation and competition with others not only results in better exploration and exploitation of problem space but also ensures faster convergence to optimal solution. The proposed SEOA based system identification approach has resolved the inherent drawbacks of premature convergence and stagnation, unlike genetic algorithm (GA), particle swarm optimisation (PSO) and differential evolution (DE). The simulation results obtained for some well known benchmark examples justify the efficacy of the proposed system identification approach using SEOA over GA, PSO and DE in terms of convergence speed, unknown plant coefficients and mean square error (MSE) values produced for both the same order and reduced order models of adaptive IIR filters.

关键词： IIR adaptive filter real coded genetic algorithm RGA particle swarm optimisation PSO differential evolution DE social emotional optimisation algorithm SEOA evolutionary optimisation techniques mean square error MSE coefficient convergence

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