Showing papers by "Durbadal Mandal published in 2017"

Optimal sizing of CMOS analog circuits using gravitational search algorithm with particle swarm optimization

Abstract: In this paper, a hybrid population based meta-heuristic search algorithm named as gravitational search algorithm (GSA) combined with particle swarm optimization (PSO) (GSA–PSO) is proposed for the optimal designs of two commonly used analog circuits, namely, complementary metal oxide semiconductor (CMOS) differential amplifier circuit with current mirror load and CMOS two-stage operational amplifier circuit. PSO and GSA are simple, population based robust evolutionary algorithms but have the problem of suboptimality, individually. The proposed GSA–PSO based approach has overcome this disadvantage faced by both the PSO and the GSA algorithms and is employed in this paper for the optimal designs of two amplifier circuits. The transistors’ sizes are optimized using GSA–PSO in order to minimize the areas occupied by the circuits and to improve the design/performance parameters of the circuits. Various design specifications/performance parameters are optimized to optimize the transistor’s sizes and some other design parameters using GSA–PSO. By using the optimal transistor sizes, Simulation Program with Integrated Circuit Emphasis simulation has been carried out in order to show the performance parameters. The simulation results justify the superiority of GSA–PSO over differential evolution, harmony search, artificial bee colony and PSO in terms of convergence speed, design specifications and performance parameters of the optimal design of the analog CMOS amplifier circuits. It is shown that GSA–PSO based design technique for each amplifier circuit yields the least MOS area, and each designed circuit is shown to have the best performance parameters like gain, power dissipation etc., as compared with those of other recently reported literature. Still the difficulties and challenges faced in this work are proper tuning of control parameters of the algorithms GSA and PSO, some conflicting design/performance parameters and design specifications, which have been partially overcome by repeated manual tuning. Multi-objective optimization may be the proper alternative way to overcome the above difficulties.

An efficient side lobe reduction technique considering mutual coupling effect in linear array antenna using BAT algorithm

Abstract: This paper presents an investigation of mutual coupling effect among the array elements in a symmetric linear array antenna with the aim of reducing the side lobe level and the null control for the radiation pattern synthesis using BAT Algorithm. PSO and DE optimization techniques are also adopted for the sake of comparison and to prove the superiority of BAT algorithm based design. Reduced side lobe level and null control, with and without considering the mutual coupling effect in the cost function have been achieved by an optimum perturbation of the array elements' current excitation amplitude weights and the inter-element spacing among the array elements. The results are also compared with those of a uniform reference array having equal number of elements with λ 2 inter-element spacing. The approach proposed in this paper is a generic one and can be easily applied to any type of symmetrical linear arrays having any number of elements. Five different design examples are presented and their performances are studied to illustrate the capability of BAT algorithm based approach over those of PSO and DE.

Optimal and accurate design of fractional-order digital differentiator – an evolutionary approach

Abstract: This study deals with the implementation of highly accurate, stable, minimum phase, and wideband fractional-order digital differentiators (FODDs) in terms of infinite impulse response filters using an efficient evolutionary optimisation algorithm called adaptive Gbest-guided gravitational search algorithm (GGSA). Performance evaluation of GGSA as compared with real coded genetic algorithm (RGA), particle swarm optimisation (PSO), and differential evolution (DE) based designs are carried out in terms of different magnitude and phase response error metrics, solution quality reliability, and convergence speed. Simulation results clearly demonstrate that GGSA significantly outperforms RGA, PSO, and DE in consistently achieving the most accurate FODDs in a computationally efficient manner. The proposed FODDs also significantly outperform all state-of-the-art designs in terms of magnitude responses.

Modeling and optimization of polymer enhanced ultrafiltration using hybrid neural-genetic algorithm based evolutionary approach

Abstract: Display Omitted Reactive red 120 dye was separated via Polymer enhanced ultrafiltration (PEUF).ANN model was developed to predict Membrane performance index (PFI).GA method used for PFI optimization was based on genetics and evolutionary biology.The hybrid ANN-GA strategy was upgraded by using hill-climbing (HC) algorithm.A PFI of 143.8L/m2h was achieved under optimal PEUF process factor settings. A stochastic genetic algorithm (GA) based strategy along with artificial neural network (ANN) was applied to optimize the retention of reactive red 120 (RR 120) dye from its aqueous solutions by way of polymer (polyethyleneimine (PEI)) enhanced ultrafiltration (PEUF). The optimal feed forward back propagation ANN (4-10-1) model network, trained initially through LevenbergMarquardt (LM) algorithm, was suitably manoeuvred by the GA approach to predict the membrane performance index (PFI) response, evaluated as the product of dye rejection and permeation flux, for a randomly generated population of chromosomes. Each chromosome was constituted by four principal genes, namely, cross-flow rate, transmembrane pressure, polymer to dye ratio, and pH. The local exploitation capacity of the canonical GA was enhanced further by combining hill-climbing (HC) local search with the optimization levels of standard GA. The near-optimal and economically feasible factor levels were predicted by the hybrid ANN-GA-HC strategy, keeping PFI maximization and the constrained PEUF process dynamics in perspective; the optimal process factor settings experimentally yielded a pragmatic PFI of 143.8L/m2h, corresponding to high (99.9%) dye rejection, and a satisfactory permeation flux (144L/m2h).

Optimal design of wideband digital integrators and differentiators using harmony search algorithm

Abstract: This paper presents an efficient approach to design stable, wideband, and infinite impulse response digital integrators (DIs) and digital differentiators (DDs) of first, second, third, and fourth order using an evolutionary optimization algorithm called harmony search (HS). In recent years, although wideband DIs and DDs have been designed using metaheuristic optimization techniques such as simulated annealing, genetic algorithm, and particle swarm optimization (PSO), these algorithms lead to sub-optimal solutions because of stagnation and premature convergence. HS algorithm, however, promises an enhanced frequency response for DIs and DDs because of the better exploration and exploitation of the search space. Simulation results demonstrate the superiority of HS-based designs as compared with three well-known benchmark evolutionary optimization algorithms, namely real coded genetic algorithm (RGA), PSO, and differential evolution (DE) based designs by yielding the least values of different magnitude response error metrics. Parametric and non-parametric statistical hypothesis tests are also conducted to compare the consistency in the performance of HS-based DIs and DDs with those of the designs based on RGA, PSO, and DE. The proposed HS-based designs also outperform those of the designs based on both classical and evolutionary optimization approaches reported in recent literature in terms of the maximum absolute magnitude error metric.

Parametric Identification with Performance Assessment of Wiener Systems Using Brain Storm Optimization Algorithm

Abstract: This paper proposes a performance assessment-based system identification of different practically useful open-loop and closed-loop Wiener systems using an evolutionary computational algorithm named as brain storm optimization (BSO) algorithm. Different performance measures of the estimation process in practical scenario, i.e., accuracy; precision; consistency; and computational time, are measured with a properly selected fitness function which is the output mean square error (MSE) between the desired and the estimated outputs. Bias and variance of the output MSE have been found negligible for each plant model to show the accuracy and consistency limits, and the corresponding statistical test results have been shown to establish the consistency of the BSO-based identification approach. Efficient identification of each plant under a noisy environment ensures the robustness and the stability of the proposed evolutionary-based identification approach with BSO. The BSO-based optimum MSE values, corresponding estimated parameter values, computational times and the other statistical information are all compared and are found to be superior to those of the other approaches reported earlier.

Opposition-based BAT algorithm for optimal design of circular and concentric circular arrays with improved far-field radiation characteristics

Abstract: In this paper, optimal designs of non-uniform single-ring circular antenna array (CAA) and non-uniform three-ring concentric circular antenna array (CCAA) have been dealt with, which gives rise to optimal improvement of far-field radiation characteristics. An evolutionary optimization technique based on opposition-based bat algorithm (OBA) is applied to determine an optimum set of current excitation weights and antenna inter-element spacing for CAA of 8, 10, and 12 elements and optimal current excitation weights for CCAA, respectively. Two three-ring CCAAs, one having the set of 4, 6, and 8 elements and the other having 8, 10, and 12 elements with and without center element, are considered. The results show a considerable reduction of side lobe level, 3-dB beamwidth, and improved directivity of CAA and better side lobe level of CCAA, with respect to the results of some recent literature reported in this paper. The BAT is a metaheuristic algorithm, based on the echolocation behavior of bats. The capability of echolocation of microbats is fascinating as the bats can find their prey and discriminate different types of insects even in complete darkness. By idealizing the echolocation behavior of bats, BAT is recently introduced in the literature. In the present paper, opposition-based learning is employed for population initialization and also for the generation jumping along with the original BAT for further improving the convergence performance of BAT. This new variant of BAT is termed as opposition-based BAT. Copyright © 2015 John Wiley & Sons, Ltd.

PSO with aging leader and challengers for optimal design of high speed symmetric switching CMOS inverter

Abstract: It is the general law of nature that every organism in the earth ages and has a limited lifespan. With the passage of time, the leader of the colony becomes old and feeble. This old leader no longer has the capability to lead the colony unless or otherwise it is challenged by a new and young challenger with great deal of enthusiasm. Thus, aging provides opportunities for the other individuals of the colony to challenge the leadership capability of the leader. This natural aging mechanism of the organism has been modelled into particle swarm optimization (PSO) and termed as PSO with aging leader and challenger (ALC-PSO). The main objective of this paper is to efficiently design a high speed symmetric switching CMOS inverter. Here, ALC-PSO is used for the optimal symmetric switching characterization of CMOS inverter. The optimal symmetric switching characterization of ALC-PSO is compared with those of real coded genetic algorithm (RGA), and conventional PSO reported in the recent literature. ALC-PSO based design results are also compared with the SPICE based results. Extensive simulation results justify the superior optimization capability of ALC-PSO over the afore-mentioned optimization techniques for the examples considered and can be efficiently used for optimal CMOS inverter design.

A hybrid backtracking search algorithm with wavelet mutation-based nonlinear system identification of Hammerstein models

Abstract: In this paper a novel and accurate approach is presented to identify varieties of nonlinear Hammerstein models (closed loop and open loop) with the help of an optimization algorithm that combines a recently proposed backtracking search algorithm with wavelet theory-based mutation scheme (BSA-WM). The optimum output MSE associated with each plant along with its statistical information justifies the better precision and accuracy of BSA-WM-based identification approach as compared to the other methods reported in earlier literature.

Generalization of Directivity Expressions for Antenna Arrays

Abstract: This communication identifies and proposes the generalized templates of directivity expressions for antenna arrays with identical omnidirectional and isotropic elements. Parameters of this expression are the element patterns, interelement distances, and the complex excitation coefficients of all elements. It is further shown that the proposed expression can also satisfactorily calculate the directivity of antenna elements. Examples show the efficiency of the proposed expressions in terms of computational speed, accuracy, and memory requirement. Simpson's 1/3rd rule is followed for numerical pattern integration.

Optimal array factor radiation pattern synthesis for linear antenna array using cat swarm optimization: validation by an electromagnetic simulator

Abstract: In this paper, an optimal design of linear antenna arrays having microstrip patch antenna elements has been carried out. Cat swarm optimization (CSO) has been applied for the optimization of the control parameters of radiation pattern of an antenna array. The optimal radiation patterns of isotropic antenna elements are obtained by optimizing the current excitation weight of each element and the inter-element spacing. The antenna arrays of 12, 16, and 20 elements are taken as examples. The arrays are designed by using MATLAB computation and are validated through Computer Simulation Technology-Microwave Studio (CST-MWS). From the simulation results it is evident that CSO is able to yield the optimal design of linear antenna arrays of patch antenna elements.

Optimal Design of Fractional-Order Digital Differentiator Using Flower Pollination Algorithm

Abstract: This paper presents an efficient approach to design wideband, accurate, stable, and minimum-phase fractional-order digital differentiators (FODDs) in terms of the infinite impulse response (IIR) filters using an evolutionary optimization technique called flower pollination algorithm (FPA). The efficiency comparisons of FPA with real-coded genetic algorithm (RGA), particle swarm optimization (PSO), and differential evolution (DE)-based designs are conducted with respect to different magnitude and phase response error metrics, parametric and nonparametric statistical hypotheses tests, computational time, and fitness convergence. Exhaustive simulation results clearly demonstrate that FPA significantly outperforms RGA, PSO, and DE in attaining the best solution quality consistently. Extensive analysis is also conducted in order to determine the role of control parameters of FPA on the performance of the designed FODDs. The proposed FPA-based FODDs outperform all the designs published in the recent literature ...

Array pattern optimization for a steerable circular isotropic antenna array using the firefly algorithm

Abstract: In this paper, a steerable isotropic circular array antenna is designed to reduce the side lobe level (SLL), using an evolutionary optimization technique. Particle swarm optimization and the firefly algorithm are used to reduce the SLL, as well as to steer the main beam in a specific direction. In this steerable circular array design, the amplitude excitations are optimized. The results show that the maximum SLL peak of the resultant patterns is as per requirements. Good performance is achieved in the array factor response and suppression of SLL for different numbers of array elements with different main beam steering angles.

Application of improved PSO for optimal design of CMOS two-stage Op-amp using nulling resistor compensation circuit

Abstract: In this paper, design of an analog VLSI circuit is proposed using an evolutionary optimization technique. Here, CMOS two-stage op-amp using nulling resistor compensation circuit is considered for the optimal design by utilizing an improved form of Particle Swarm Optimization (PSO) method that is Craziness based Particle Swarm Optimization (CRPSO). The concept of PSO is simple and it replicates the nature of bird flocking. As compared to Genetic algorithm (GA), PSO deals with less mathematical operators. Premature convergence and stagnation problem are the two major limitations of PSO technique. PSO has been already been improved to CRPSO to eliminate the limitations of PSO and is now applied for the optimal design of analog VLSI circuit in this paper. Control parameters of CRPSO are nearly robust and it produces near-global convergence. In this work, CRPSO is used to optimize the sizes of the MOS transistors' to minimize the overall area occupied by the circuit. The results obtained from CRPSO technique are validated with SPICE. SPICE based simulation results show that CRPSO is much better technique than previously reported techniques for the design of above mentioned circuit in terms of MOS area, gain, power dissipation etc.

Artificial Raindrop Algorithm-Based Design of Wideband IIR Fractional Order Digital Integrators

Abstract: A new technique to design accurate, stable, and wideband infinite impulse response, fractional order digital integrators (FODIs) for the fractional order integrators based on a metaheuristic optimization technique called artificial raindrop algorithm (ARA) is proposed in this paper. ARA is inspired by the process of natural rainfall, and achieves the global optimal solution by identifying the raindrop which occupies the lowest altitude. To investigate the efficiency of the proposed approach, comparisons have been carried out with real coded genetic algorithm, particle swarm optimization, and differential evolution-based FODIs. Simulation results demonstrate that ARA-based FODIs consistently accomplish better magnitude responses in the least number of iteration cycles. The proposed FODIs also outperform all the recently reported designs.

Enhanced colliding bodies optimisation-based optimal design of wideband digital integrators and differentiators

Abstract: This paper presents an efficient approach to determine the optimal set of coefficients for the design of wideband infinite impulse response (IIR) digital integrators (DIs) and digital differentiators (DDs) of first, second, third, and fourth order, meeting the accurate magnitude response specification, using a recently proposed evolutionary optimisation algorithm called enhanced colliding bodies optimisation (ECBO). To demonstrate the effectiveness of the proposed approach, the results of the ECBO-based designs have been compared with those of eight other nature-inspired metaheuristic optimisation algorithms. Parametric and non-parametric statistical hypothesis tests are conducted to validate the consistency of the performance of the ECBO-based DIs and DDs. Simulation results demonstrate that ECBO-based designs achieve the least absolute magnitude error and demonstrate a competitive group delay response of the designed DIs and DDs of different orders as compared with the designs based on the competing algorithms. The proposed DIs and DDs also outperform those of the design approaches published in literature and achieve the best responses in terms of the maximum absolute magnitude error over a wide frequency range.

Optimal Design of Single and Multi-ring Planar Array Antenna Using Simplex-PSO

Abstract: In this paper, optimal design of single-ring and multi-ring circular array, hexagonal array, and elliptical array of isotropic antenna has been carried out using Simplex Particle Swarm Opti...

Directivity improvement and optimal far field pattern of time modulated concentric circular antenna array using hybrid evolutionary algorithms

Abstract: In this paper time modulated nine-ring concentric circular antenna array (TMCCAA) using fitness based novel hybrid adaptive differential evolution with particle swarm optimization (ADEPSO) has been studied. ADEPSO is hybrid of fitness based adaptive differential evolution and particle swarm optimization (PSO). Differential evolution is a simple and robust evolutionary algorithm but sometimes causes instability problem; PSO is also a simple, population based robust evolutionary algorithm but has the problem of sub-optimality. ADEPSO has overcome the above individual disadvantages faced by both the algorithms and is used for the design of TMCCAA. The comparative case studies as Case-1 and Case-2 are made with three control parameters like uniform inter-element spacing in rings, inter-ring radii and the switching “ON” times of rings. The same array radiates at various harmonic frequencies. The first two harmonic frequencies have been considered in this work. The numerical results show Case-2, outperforms Case-1 with respect to better side lobe level (SLL), and more improved directivity. Apart from this, the authors have computed powers radiated at the center/fundamental frequency and the first two sideband frequencies, and dynamic efficiency. It is found that power radiated by any sideband frequency is very less as compared with the power radiated at the center frequency. It has been observed that as the sideband frequency increases, side band level decreases to the greater extent as compared with SLL. The aperture size is a very important constraint for the array, since there is an upper limit for the aperture size of a given array in real-life environment. Hence, in our optimization design, the maximum radius of the concentric ring array is constrained.

Array pattern optimization for steerable linear isotropic antenna array using novel particle swarm optimization

Abstract: In this paper, the beam steerable isotropic Linear Antenna Array is designed for reducing the side lobe level using evolutionary optimization technique. The optimization techniques particle swarm optimization (PSO) and Novel PSO are used to reduce the side lobe level (SLL) as well as to steer the main beam in specific direction. In this design of steerable Linear arrays, the amplitude excitations are optimized. Obtained results show that the maximum peak of SLL of the resultant patterns is as per requirement. This paper presents a good performance in the array factor response and suppressed SLL for different numbers of elements and different values of the steering angle of direction of the main beam with evolutionary optimization.

Optimal Design of Elliptical Array Antenna Using Opposition Based Differential Evolution Technique

Abstract: Radiation pattern synthesis of non-uniformly excited planar arrays with the lowest relative side lobe level (SLL) is presented in this paper. Opposition based differential evolution (ODE) scheme, which represents a novel parameter optimization technique in antenna engineering is applied for the parameter optimization of the single and the multi-ring circular array (CA), hexagonal array (HA) and elliptical array (EA) of isotropic elements. To overcome the problem of premature convergence of differential evolution (DE) algorithm, ODE is designed without significantly impairing the fast converging property of DE. Two design examples are presented which illustrate the effectiveness of the ODE based method, and the optimization goal for each example is easily achieved. The design results obtained using ODE are much more improved than those of the results obtained using the state of the art evolution algorithms like particle swarm optimization (PSO), harmonic search (HS) and differential evolution (DE) methods in a statistically significant way.

Simultaneous Improvement of array factor directivity and side lobe level of time‐modulated linear antenna arrays using opposition‐based harmony search algorithm

Abstract: Summary In this paper, simultaneous improvement of array factor directivity and side lobe level of time-modulated linear antenna arrays using opposition-based harmony search algorithm has been dealt with. Because of the periodic function of switching time, the same antenna array will radiate at fundamental (center) frequency as well as its harmonic frequencies. The first two harmonic frequencies are (f0 + Fp) and (f0 + 2Fp), where f0 and Fp are operating frequency and pulse repetition frequency, respectively. Four case studies have been adapted; Case-1: optimal switching time sequence of each element; Case-2: optimal switching time sequence of each element and optimal non-uniform inter-element spacing; Case-3: optimal switching time sequence of each element, optimal excitation phase of each element, and optimal uniform inter-element spacing; Case-4 refers to optimal switching time sequence of each element and optimal uniform inter-element spacing. Simulation results reflect that Case-4 outperforms Case-1, Case-2, and Case-3. The objective function was judiciously chosen in such a way that it can simultaneously improve the array factor directivity as well as side lobe level. Considered for the analysis was 16-element linear antenna array. Various simulation results are presented showing better side lobe performance, better side band performance, and improved array factor directivity with respect to the uniform array having the same number of elements. The numerical results show the power radiated by any harmonic frequency is less as compared with the power radiated at the center frequency called the fundamental frequency. It has also been observed that as the harmonic frequency increases, sideband level and power radiated by the antenna at its harmonic frequency decrease. Copyright © 2015 John Wiley & Sons, Ltd.

Optimal design of 5.5 GHz CMOS LNA using hybrid fitness based adaptive De with PSO

Abstract: This paper presents a novel approach for the optimal design of a Low Noise Amplifier (LNA) with inductive source degeneration circuit using a novel hybrid optimization technique called fitness based adaptive differential evolution with particle swarm optimization (ADEPSO). The simulation results obtained for the designed LNA confirm the effectiveness of the ADEPSO based approach over PSO in terms of the solution quality, design specifications and design objectives. The optimally designed CMOS LNA circuit implemented in 0.18 μm CMOS technology yields a gain of 22.11 dB and the noise figure of 0.799 dB and the power dissipation of 6.6 mW.

Nature-inspired Algorithm-based Optimization for Beamforming of Linear Antenna Array System

Abstract: Nature-inspired algorithms have brought great revolution in all fields of electromagnetics where the optimization of certain parameters is highly complex and nonlinear. With the help of proper design of the cost function or the fitness function in terms of optimizing parameters, any type of problem can be solved. The nature-inspired algorithms play an important role in the optimal design of antenna array with better radiation characteristics. In this work, hyper-beamforming of linear antenna array has been taken as an example of nature- inspired optimization in antenna array system. An emerging nature-inspired optimization technique has been applied to design the optimal array to reduce the side lobes and to improve the other radiation characteristics to show the effect of the optimization on design via the nature-inspired algorithms. Various nature-inspired algorithms have been considered for the optimization. Flower pollination algorithm (FPA) is applied to determine the optimal amplitude coefficients and the spacing between the elements of the array of the optimized hyper-beamforming of linear antenna array. FPA keeps the best solution until it reaches the end of the iteration. The results obtained by the FPA algorithm have been compared with those of other stochastic algorithms, such as real-coded genetic algorithm (RGA), particle swarm optimization (PSO), differential evolution (DE), firefly algorithm (FFA), simulated annealing (SA), artificial immune system (AIS), and artificial bee colony (ABC). Optimal hyper-beamforming of the same obtained by FPA can obtain the best improvement in side lobe level (SLL) with fixed first null beam width (FNBW). Directivity of the array is calculated by using Simpsons 1/3 rule. The entire simulation has been done for 10-, 14-, and 20-element linear antenna arrays.

Evolutionary Computation Based Sizing Technique of Nulling Resistor Compensation Based CMOS Two-Stage Op-Amp Circuit

Abstract: This article explores the comparative optimizing efficiency between two PSO variants, namely, Craziness based PSO (CRPSO) and PSO with an Aging Leader and Challengers (ALC-PSO) for the design of nulling resistor compensation based CMOS two-stage op-amp circuit. The concept of PSO is simple and it replicates the nature of bird flocking. As compared with Genetic algorithm (GA), PSO deals with less mathematical operators. Premature convergence and stagnation problem are the two major limitations of PSO technique. CRPSO and ALC-PSO techniques individually have eliminated the disadvantages of the PSO technique. In this article, CRPSO and ALC-PSO are individually employed to optimize the sizes of the MOS transistors to reduce the overall area taken by the circuit while satisfying the design constraints. The results obtained individually from CRPSO and ALC-PSO techniques are validated in SPICE environment. SPICE based simulation results justify that ALC-PSO is much better technique than CRPSO and other formerly ...

Null Placement in Time-Modulated Linear Antenna Arrays of Dipole Element

Abstract: This paper describes the practical implementation of time-modulated linear dipole antenna arrays (TMLDAAs) using EM simulator. Cat swarm optimization (CSO) algorithm is applied to improve the null performance of TMLDAAs by radio frequency switch. The nulls at the particular positions of the direction of arrival of a TMLDAA can be reduced effectively by the proper optimization of current excitation weights and an effective design of switch-on time intervals of each element. CSO gives the optimized value of the current excitation weights of the radiation pattern. The optimized results obtained by CSO algorithm using MATLAB simulation are practically implemented and are designed through computer simulation technology–microwave studio simulation software for half-wave dipole antenna arrays. The dipole antenna is designed to operate at a resonant frequency of 2 GHz. The antenna exhibits a frequency band from 1.8331 to 2.1285 GHz, which is applicable for wireless applications.

Optimal Design of Low-Noise Three-Stage Op-amp Using PSO Algorithm

Abstract: This paper adopts the bio-inspired Particle Swarm Optimization (PSO) algorithm for the design of a low-noise three-stage CMOS operational amplifier (TSCOA) circuit. The concept of PSO relies on the communal manner of bird flocking techniques. It is a very simple and easy to implement. The contribution of this work is to optimize the sizes of the individual MOS transistors by using PSO to reduce overall area of the circuit as well as the power dissipation. The optimized results are confirmed by Cadence simulator. The Cadence (IC 510) simulated results show that the design specifications are accurately met and the necessary functionalities are achieved. PSO-based design results in an improved functionality compared to those of the results reported in the recent literature.

Wide null control of compact multiple antenna terminals using PSO

Abstract: As the microwave spectrum becomes more and more crowded with users, interference rejection techniques become increasingly necessary. One way to reduce the interference is to introduce a point of minimum signal reception in the interference direction of antenna radiation pattern. This article presents the synthesis of non-uniformly spaced linear array with reduction of side lobe levels while controlling the beam width and the points of minimum signal reception by applying Particle Swarm Optimization (PSO) technique. The algorithm is implemented to ascertain an optimal separation between the antenna elements that contributes deeper nulls to the radiation pattern in a specified range of direction. The effectiveness of algorithm is compared and presented in the form of graphs and tables. Spacing between two successive elements, d is taken to be between λ/4 and λ/8 thus enabling the design of compact multiple antenna terminals.

Optimal Design of Low-Voltage, Two-Stage CMOS Op-amp Using Evolutionary Techniques

Abstract: This article explores the comparative optimizing efficiency of particle swarm optimization (PSO) and simplex-PSO method for the design of a low-voltage, two-stage CMOS op-amp. The concept of PSO is based on communal manner of bird flocking. The disadvantages of PSO are premature convergence and stagnation problem. Simplex-PSO is the combination of Nelder–Mead simplex method and PSO without considering the velocity term. The main idea is to optimize the size of the MOS transistors used for the op-amp circuit to reduce the overall area of the circuit. PSO- and simplex-PSO-based optimized results are confirmed by SPICE-based simulation. SPICE simulation results show that design specifications are approximately met and necessary functionalities are achieved. Simplex-PSO shows the better optimizing efficiency than PSO for the designed circuit.