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

2017 ◽  
Vol 26 (04) ◽  
pp. 1740021 ◽  
Author(s):  
Bishnu Prasad De ◽  
Kanchan Baran Maji ◽  
Rajib Kar ◽  
Durbadal Mandal ◽  
Sakti Prasad Ghoshal
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Computation ◽  
Power Dissipation ◽  
Premature Convergence ◽  
Mos Transistors ◽  
Two Stage ◽  
Design Constraints ◽  
Op Amp ◽  
Simulation Results

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 reported methods for the design of the afore mentioned circuit in terms of the MOS area, gain and power dissipation etc.

New Op-Amp Circuits Realizations Using Genetic Algorithm

2017 ◽  
Vol 26 (09) ◽  
pp. 1750131 ◽  
Author(s):  
Nariman A. Khalil ◽  
Rania F. Ahmed ◽  
Rania A. Abul-seoud ◽  
Ahmed M. Soliman
Keyword(s):  
Genetic Algorithm ◽  
Type A ◽  
Symbolic Representation ◽  
Equivalent Circuits ◽  
Analog Design ◽  
Op Amp ◽  
Simulation Results ◽  
Original Configuration ◽  
Filter Configuration

Genetic Algorithm (GA) applications in analog design circuits play an important role with promising results. This paper introduces a proposed methodology based on the genetic algorithm and the symbolic representation to generate equivalent op-amp configurations for well-known filters. The proposed methodology is applied to the Tow-Thomas (TT) filter to generate 168 different configurations. Moreover, it is also applied on the KHN filter resulting in 30 equivalent circuits for each type. A part of the generated realizations is tested through simulations using PSPICE simulator and the simulation results determine the number of accepted circuits. A simulation comparison between the original filter configuration and some of the accepted configurations is done. Fortunately, a better performance compared to the original configuration is obtained from some generated circuits.

Multi Population Genetic Algorithm on Solving Multimodal Function

2013 ◽  
Vol 321-324 ◽  
pp. 2042-2046
Author(s):  
Hao Wen ◽  
Han Bin Chen
Keyword(s):  
Genetic Algorithm ◽  
Population Genetic ◽  
Extreme Value ◽  
Premature Convergence ◽  
Multimodal Function ◽  
Peak Function ◽  
Simulation Results ◽  
Population Genetic Algorithm ◽  
Standard Genetic Algorithm ◽  
The Way

This paper studies the way to solve extreme value of the nonlinear multi-peak function by using the multi-population genetic algorithm (MPGA). With the analysis of the advantages and defects of the standard genetic algorithm (SGA),the paper, This paper is use the population genetic algorithm to achieve the optimization and verification with Simulation for solving the extreme value of the nonlinear multi-peak function, which in order to achieve the solution with higher accuracy and higher efficiency. And make the analysis for the premature convergence that existed in SGA by comparing the standard genetic algorithm simulation results with that form the MPGA.

scholarly journals Coevolution of Artificial Agents Using Evolutionary Computation in Bargaining Game

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
Sangwook Lee
Keyword(s):  
Genetic Algorithm ◽  
Game Theory ◽  
Particle Swarm Optimization ◽  
Evolutionary Computation ◽  
Bargaining Game ◽  
The Other ◽  
Artificial Agents ◽  
Swarm Optimization ◽  
Simulation Results ◽  
Game Performance

Analysis of bargaining game using evolutionary computation is essential issue in the field of game theory. This paper investigates the interaction and coevolutionary process among heterogeneous artificial agents using evolutionary computation (EC) in the bargaining game. In particular, the game performance with regard to payoff through the interaction and coevolution of agents is studied. We present three kinds of EC based agents (EC-agent) participating in the bargaining game: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). The agents’ performance with regard to changing condition is compared. From the simulation results it is found that the PSO-agent is superior to the other agents.

RBF Network Based on Improved Niche Hybrid Hierarchy Genetic Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 775-780
Author(s):  
Fang Wang ◽  
Jin Lan Yu ◽  
Pin Chang Zhu ◽  
Xi Feng Wei
Keyword(s):  
Genetic Algorithm ◽  
Time Series ◽  
Chaotic Time Series ◽  
Premature Convergence ◽  
Rbf Network ◽  
Operation Speed ◽  
System Resource ◽  
Simulation Results

The improved niche hybrid hierarchy genetic algorithm is presented to overcome the premature convergence which happens in genetic algorithm constructing RBF network. The niche with poor fitness of every individual is eliminated to save system resource and raise operation speed. The simulation results demonstrate the better predicted performance on the Mackey-Glass chaotic time series than other algorithms.

Optimization of Power and Gain in Two-Stage Op-Amp by Using Taguchis Approach

2013 ◽  
Vol 712-715 ◽  
pp. 1820-1825 ◽  
Author(s):  
Siti Amaniah Mohd Chachuli ◽  
Faiz Arith ◽  
Mohammad Idzdihar Idris
Keyword(s):  
Taguchi Method ◽  
Power Dissipation ◽  
Statistical Approach ◽  
Circuit Simulation ◽  
Power Reduction ◽  
Total Power ◽  
Two Stage ◽  
Op Amp

This paper presents a method based on statistical approach which known as Taguchi method. This method is used to optimize power dissipations and gain in a two-stage op-amp. Standard L27 which uses three factors and two outputs is chosen to optimize power and gain in the circuit. Simulation of the circuit has been implemented by using Mentor Graphics DA-IC. From the simulation, the results showed that total power dissipation has decreased from 3.9643 mW to 1.0345 mW. The percentage of power reduction is 73.9%. The overall gain also has been improved from 22 dB to 45.49 dB. The percentage of increment gain in two-stage op-amp is 56%.

Implementation of an Ultra Low Power Process-Insensitive Two Stage Complementary Metal Oxide Semiconductor Operational Amplifier with Enhanced Direct Current Gain at 45 nm Technology Node

2020 ◽  
Vol 18 (10) ◽  
pp. 770-775
Author(s):  
Pragati Gupta ◽  
Shyam Akashe
Keyword(s):  
Low Power ◽  
Power Dissipation ◽  
Performance Metrics ◽  
Current Gain ◽  
Phase Margin ◽  
Two Stage ◽  
Gain Bandwidth ◽  
Ultra Low Power ◽  
Op Amp ◽  
Dc Gain

This paper presents an ultra low power process-insensitive two stage CMOS OP-AMP employing bulk-biasing technique realised in a standard 45 nm CMOS technology. Bulk-Biasing technique has been employed to augment the DC gain of two stage CMOS OP-AMP without having any impact on its power dissipation and output swing. In this work, high gain-bandwidth product (GBW) with appropriate phase margin is achieved through pseudo-cascode compensation approach which overcomes the drawbacks of Miller compensation technique also. Furthermore, the effect of width scaling on performance metrics of proposed OP-AMP has been analysed. The designed OP-AMP exhibits enhanced DC gain of 94.2 dB, gain-bandwidth product (GBW) of 460 MHz and adequate phase margin of 80°; with fast settling response. Also, the proposed OP-AMP has power dissipation of 27 μW and leakage current of 6.4 pA only. The design and optimisation of proposed OP-AMP is carried out at a power supply of 0.7 V under room temperature in Cadence Virtuoso tool.

Factual Power Loss Diminution by Enhanced Frog Leaping Algorithm

2020 ◽  
Vol 3 (2) ◽  
pp. 114-118
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Local Search ◽  
Power Loss ◽  
Reactive Power ◽  
Test System ◽  
Premature Convergence ◽  
Speed Of Convergence ◽  
Real Power ◽  
The Real ◽  
Simulation Results ◽  
Power Problem

This paper proposes Enhanced Frog Leaping Algorithm (EFLA) to solve the optimal reactive power problem. Frog leaping algorithm (FLA) replicates the procedure of frogs passing though the wetland and foraging deeds. Set of virtual frogs alienated into numerous groups known as “memeplexes”. Frog’s position’s turn out to be closer in every memeplex after few optimization runs and certainly, this crisis direct to premature convergence. In the proposed Enhanced Frog Leaping Algorithm (EFLA) the most excellent frog information is used to augment the local search in each memeplex and initiate to the exploration bound acceleration. To advance the speed of convergence two acceleration factors are introduced in the exploration plan formulation. Proposed Enhanced Frog Leaping Algorithm (EFLA) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss considerably.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

scholarly journals A Two-Stage Mono- and Multi-Objective Method for the Optimization of General UPS Parallel Manipulators

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 543
Author(s):  
Alejandra Ríos ◽  
Eusebio E. Hernández ◽  
S. Ivvan Valdez
Keyword(s):  
Genetic Algorithm ◽  
Performance Metrics ◽  
Tracking Error ◽  
Parallel Manipulators ◽  
Optimization Method ◽  
Statistical Hypothesis ◽  
Two Stage ◽  
Multi Objective ◽  
Second Stage ◽  
Conflicting Objectives

This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests.

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