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.

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.

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.

A novel identification approach for shearer running status through integration of rough sets and improved wavelet neural network

2016 ◽  
Vol 230 (16) ◽  
pp. 2792-2805 ◽  
Author(s):  
Lei Si ◽  
Zhongbin Wang ◽  
Xinhua Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Rough Sets ◽  
Particle Swarm Optimization Algorithm ◽  
Wavelet Neural Network ◽  
Decision Table ◽  
Swarm Optimization ◽  
Novel Approach ◽  
Identification Approach ◽  
Simulation Results

In order to accurately and conveniently identify the shearer running status, a novel approach based on the integration of rough sets (RS) and improved wavelet neural network (WNN) was proposed. The decision table of RS was discretized through genetic algorithm and the attribution reduction was realized by MIBARK algorithm to simply the samples of WNN. Furthermore, an improved particle swarm optimization algorithm was proposed to optimize the parameters of WNN and the flowchart of proposed approach was designed. Then, a simulation example was provided and some comparisons with other methods were carried out. The simulation results indicated that the proposed approach was feasible and outperforming others. Finally, an industrial application example of mining automation production was demonstrated to verify the effect of proposed system.

Collaborative Scheduling of Algorithms for Path Planning of Unmanned Systems

2021 ◽  
Vol 01 ◽  
Author(s):  
Ying Li ◽  
Chubing Guo ◽  
Jianshe Wu ◽  
Xin Zhang ◽  
Jian Gao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Unmanned Systems ◽  
Ant Colony Optimization Algorithm ◽  
A Algorithm ◽  
Collaborative Scheduling ◽  
Simulation Results ◽  
Planning Problems ◽  
Effective Path

Background: Unmanned systems have been widely used in multiple fields. Many algorithms have been proposed to solve path planning problems. Each algorithm has its advantages and defects and cannot adapt to all kinds of requirements. An appropriate path planning method is needed for various applications. Objective: To select an appropriate algorithm fastly in a given application. This could be helpful for improving the efficiency of path planning for Unmanned systems. Methods: This paper proposes to represent and quantify the features of algorithms based on the physical indicators of results. At the same time, an algorithmic collaborative scheme is developed to search the appropriate algorithm according to the requirement of the application. As an illustration of the scheme, four algorithms, including the A-star (A*) algorithm, reinforcement learning, genetic algorithm, and ant colony optimization algorithm, are implemented in the representation of their features. Results: In different simulations, the algorithmic collaborative scheme can select an appropriate algorithm in a given application based on the representation of algorithms. And the algorithm could plan a feasible and effective path. Conclusion: An algorithmic collaborative scheme is proposed, which is based on the representation of algorithms and requirement of the application. The simulation results prove the feasibility of the scheme and the representation of algorithms.

scholarly journals Economic Environmental Dispatch of Wind Integrated Thermal Power System

2020 ◽  
Vol 8 (6) ◽  
pp. 5186-5192
Keyword(s):  
Genetic Algorithm ◽  
Power Plant ◽  
Thermal Power ◽  
Nsga Ii ◽  
Strength Pareto Evolutionary Algorithm ◽  
Experimental Constraint ◽  
Thermal Power System ◽  
Power Plant Operation ◽  
Simulation Results ◽  
The Given

In electric power plant operation, Economic Environmental Dispatch (EED) of a thermal-wind is a significant chore to involve allocation of production amongst the running units so the price, NOx extraction status and SO2 extraction status are enhanced concurrently whilst gratifying each and every experimental constraint. This is an exceedingly controlled multiobjective optimizing issue concerning contradictory objectives having Primary and Secondary constraints. For the given work, a Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is recommended for taking care of EED issue. In simulation results that are obtained by applying the two test systems on the proposed scheme have been evaluated against Strength Pareto Evolutionary Algorithm 2 (SPEA 2).

Modeling Knowledge Employee’s Turnover Based on P-SVM

2010 ◽  
Vol 121-122 ◽  
pp. 825-831
Author(s):  
Yong Zhao ◽  
Ye Zheng Liu
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Model Development ◽  
Forecast Model ◽  
Support Vector ◽  
Multi Criteria Decision Making ◽  
Parameters Selection ◽  
Vector Machines ◽  
Decision Making Problem ◽  
Simulation Results

Knowledge employee’s turnover forecast is a multi-criteria decision-making problem involving various factors. In order to forecast accurately turnover of knowledge employees, the potential support vector machines(P-SVM) is introduced to develop a turnover forecast model. In the model development, a chaos algorithm and a genetic algorithm (GA) are employed to optimize P-SVM parameters selection. The simulation results show that the model based on potential support vector machine with chaos not only has much stronger generalization ability but also has the ability of feature selection.

Genetic-Algorithm-Based Optimization of Fragile Watermarking in Discrete Hartley Transform Domain

2016 ◽  
pp. 103-127
Author(s):  
Sudipta Kr Ghosal ◽  
Jyotsna Kumar Mandal
Keyword(s):  
Genetic Algorithm ◽  
Color Images ◽  
Fragile Watermarking ◽  
Transform Domain ◽  
One Dimensional ◽  
Discrete Hartley Transform ◽  
Hartley Transform ◽  
Message Digest ◽  
Simulation Results ◽  
Watermarking Scheme

In this chapter, a fragile watermarking scheme based on One-Dimensional Discrete Hartley Transform (1D-DHT) has been proposed to verify the authenticity of color images. One-Dimensional Discrete Hartley Transform (1D-DHT) converts each 1 x 2 sub-matrix of pixel components into transform domain. Watermark (along with a message digest MD) bits are embedded into the transformed components in varying proportion. To minimize the quality distortion, genetic algorithm (GA) based optimization is applied which yields the optimized component corresponding to each embedded component. Applying One-Dimensional Inverse Discrete Hartley Transform (1D-IDHT) on 1 x 2 sub-matrices of embedded components re-generates the pixel components in spatial domain. The reverse approach is followed by the recipient to retrieve back the watermark (along with the message digest MD) which in turn is compared against the re-computed Message Digest (MD') for authentication. Simulation results demonstrate that the proposed technique offers variable payload and less distortion as compared to existing schemes.

scholarly journals Validation of a Dynamic Planning Navigation Strategy Applied to Mobile Terrestrial Robots

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4322 ◽  
Author(s):  
Caroline Silva ◽  
Átila de Oliveira ◽  
Marcelo Fernandes
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Autonomous Navigation ◽  
Genetic Parameters ◽  
Dynamic Environments ◽  
Dynamic Planning ◽  
Navigation Algorithm ◽  
Navigation Strategy ◽  
Terrestrial Environments ◽  
Simulation Results

This work describes the performance of a DPNA-GA (Dynamic Planning Navigation Algorithm optimized with Genetic Algorithm) algorithm applied to autonomous navigation in unknown static and dynamic terrestrial environments. The main aim was to validate the functionality and robustness of the DPNA-GA, with variations of genetic parameters including the crossover rate and population size. To this end, simulations were performed of static and dynamic environments, applying the different conditions. The simulation results showed satisfactory efficiency and robustness of the DPNA-GA technique, validating it for real applications involving mobile terrestrial robots.

scholarly journals Motion Planning Using a Memetic Evolution Algorithm for Swarm Robots

10.5772/45669 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 19 ◽  
Author(s):  
Chien-Chou Lin ◽  
Kun-Cheng Chen ◽  
Wei-Ju Chuang
Keyword(s):  
Genetic Algorithm ◽  
Formation Control ◽  
Memetic Algorithm ◽  
Initial Population ◽  
Local Motion ◽  
Computation Efficiency ◽  
Swarm Robots ◽  
Evolution Algorithm ◽  
Simulation Results ◽  
Path Planner

A hierarchical memetic algorithm (MA) is proposed for the path planning and formation control of swarm robots. The proposed algorithm consists of a global path planner (GPP) and a local motion planner (LMP). The GPP plans a trajectory within the Voronoi diagram (VD) of the free space. An MA with a non-random initial population plans a series of configurations along the path given by the former stage. The MA locally adjusts the robot positions to search for better fitness along the gradient direction of the distance between the swarm robots and the intermediate goals (IGs). Once the optimal configuration is obtained, the best chromosomes are reserved as the initial population for the next generation. Since the proposed MA has a non-random initial population and local searching, it is more efficient and the planned path is faster compared to a traditional genetic algorithm (GA). The simulation results show that the proposed algorithm works well in terms of path smoothness and computation efficiency.

Optimization of Interval-Based Back Surface Water Cooling For Photovoltaic/Thermal Systems

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Himanshu Sainthiya ◽  
Navneet Garg ◽  
Narendra S. Beniwal
Keyword(s):  
Genetic Algorithm ◽  
Surface Water ◽  
Power Consumption ◽  
Back Surface ◽  
Water Cooling ◽  
Interval Duration ◽  
Pv System ◽  
Thermal Systems ◽  
Pumping System ◽  
Simulation Results

Abstract The efficiency of photovoltaic (PV) cells degrades, when the temperature increases more than a certain limit. To maintain the temperature within the limit, we consider and analyze a back surface-based water cooled PV system. This analysis shows that the cell temperatures are proportional to the negative exponent of the water flowrates and higher flowrates increase the power consumption. Keeping this in mind, we present interval-based cooling in order to reduce the total consumed power. Moreover, the active pump duration and water flowrates are optimized to maximize the electrical efficiency of the PV system. Due to non-convex nature of the objective function, the Genetic algorithm is employed to get the solutions. Simulation results show that the optimized water flowrate for a given interval duration minimizes the consumed power in pumping system, while maintaining the temperatures within the limit.

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