检索结果-内蒙古大学图书馆

Hybrid algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Reservoir Operation Management

WATER RESOURCES MANAGEMENT 2020年第15期34卷 4545-4560页

作者： Dahmani, Saad Yebdri, Djilali Univ Sci & Technol Oran Mohamed Boudiaf USTO MB Lab Gest & Traitement Eaux LGTE BP 1505 Oran 31000 Algeria

Metaheuristics are highly efficient optimization methods that are widely used today. However, the performance of one method cannot be generalized and must be examined in each class of problems. The hybrid algorithm of particle swarm optimization and grey wolf optimizer (HPSOGWO) is new swarm-based metaheuristic with several advantages, such as simple implementation and low memory consumption. This study uses HPSOGWO for reservoir operation optimization. Real-coded genetic algorithm (RGA) and gravitational search algorithm (GSA) have been used as efficient methods in reservoir optimization management for comparative analysis between algorithms through two case studies. In the first case study, four benchmark functions were minimized, in which results revealed that HPSOGWO was more competitive compared with other algorithms and can produce high-quality solutions. The second case study involved minimizing the deficit between downstream demand and release from the Hammam Boughrara reservoir located in Northwest Algeria. A constrained optimization model with non-linear objective function was applied. Based on the average solutions, HPSOGWO performed better compared with RGA and was highly competitive with GSA. In addition, the reliability, resiliency, and vulnerability indices of the reservoir operation, which was derived from the three algorithms, were nearly similar to one another, which justified the usability of HPSOGWO in this field.

关键词： Reservoir optimization model Evolutionary algorithm Genetic algorithm gravitational search algorithm Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

Newtonian mechanics based hybrid machine learning method of characterizing energy band gap of doped zno semiconductor

引用

CHINESE JOURNAL OF PHYSICS 2020年 68卷 493-506页

作者： Ibn Shamsah, Sami M. Owolabi, Taoreed O. Univ Hafr Al Batin Coll Engn Dept Mech Engn POB 1803 Hafar al Batin 31991 Saudi Arabia Adekunle Ajasin Univ Phys & Elect Dept Akungba Akoko 342111 Ondo State Nigeria

This present work utilizes the distortion due to the incorporation of dopants into crystal lattice structure of zinc oxide (ZnO) semiconductor to model the associated energy band gap. The proposed model hybridizes machine learning support vector regression model (MLSVRM) and gravitational search algorithm (GSA) that is based on Newtonian mechanical principle of motion. The performance of the developed hybrid gravitational search (GS) based MLSVRM is compared with stepwise regression (ST) based model and the existing ordinary support vector regression computational intelligence (SVRCI) model in the literature. The developed hybrid GS-MLSVRM performs better than ST-based model and the existing SVRCI model in the literature by 83.74% and 42.59%, respectively using mean absolute percentage deviation (MAPD) as a performance measuring parameter. The developed hybrid GS-MLSVRM further shows superior performance over the two compared models using other performance measuring parameters such as root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (CC). The superior performance of the developed hybrid model can be attributed to the power of hybridization and intrinsic ability to approximate non-linear relationship between lattice distortion and energy band gap of the doped semiconductor. The precision of the developed hybrid model would ultimately hasten ZnO band gap characterization without experimental stress.

关键词： Band gap energy Hybrid model gravitational search algorithm Support vector regression and crystal lattice parameters

来源：评论

学校读者我要写书评

暂无评论

Observer-based control for UPQC-S with optimized gains of PI controller

引用

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2020年第7期30卷 1-22页

作者： Alam, Sayed Javed Arya, Sabha Raj Sardar Vallabhai Natl Inst Technol Dept Elect Engn Surat 395007 India

A new control algorithm based on observer for Unified Power Quality Conditioner (UPQC-S) is discussed which named as an adaptive observer design under various power qualities (PQ) perturbations. This adaptive observer based method is designed to estimate supply voltage parameters which includes its amplitude, frequency, and the phase angle with zero steady-state error. These quantities are real time control and mitigation of PQ issues. Currently, for estimation of these parameters Phase Lock Loop (PLL) or its derivates are adopted. However, they are local asymptotically stable, hardly achieving fast response and its sensitively decreases when voltage fluctuates. Whereas, the adaptive observer used here is theoretically proven to be asymptotic-stable globally, which ensures that it has faster dynamic response for robust estimation of grid parameters. In this article, reference signals are extracted through adaptive observer based algorithm to get the gating pulses for both the active power filters of UPQC-S. It improves the PQ of the line by mitigating harmonics elimination, reactive power compensation voltage sag/swell and power factor correction with optimized tuned value of PI gains. The performance analysis of UPQC-S is found satisfactory with different PQ perturbations.

关键词： adaptive observer APF global stable gravitational search algorithm PI-tuning power quality UPQC-S

来源：评论

学校读者我要写书评

暂无评论

Stability enhancement of wind energy integrated hybrid system with the help of static synchronous compensator and symbiosis organisms search algorithm

引用

Protection and Control of Modern Power Systems 2020年第1期5卷 153-165页

作者： Pabitra Kumar Guchhait Abhik Banerjee Department of Electrical Engineering National Institute of TechnologyYupiaArunachal PradeshIndia

Conventional proportional integral derivative(PID)controllers are being used in the industries for control *** is very simple in design and low in cost but it has less capability to minimize the low frequency noises of the ***,in this study,a low pass filter has been introduced with the derivative input of the PID controller to minimize the noises and to improve the transient stability of the *** paper focuses upon the stability improvement of a wind-diesel hybrid power system model(HPSM)using a static synchronous compensator(STATCOM)along with a secondary PID controller with derivative filter(PIDF).Under any load disturbances,the reactive power mismatch occurs in the HPSM that affects the system transient *** with PIDF controller is used to provide reactive power support and to improve stability of the *** controller parameters are also optimized by using soft computing technique for performance *** paper proposes the effectiveness of symbiosis organisms search algorithm for optimization *** coded genetic algorithm and gravitational search algorithm are used for the sake of comparison.

关键词： Binary coded genetic algorithm gravitational search algorithm Hybrid power system model PID controller with derivative filter Static synchronous compensator Symbiosis organisms search algorithm Transient stability

来源：评论

学校读者我要写书评

暂无评论

Optimization of constructal T-shaped porous fins under convective environment using Swarm Intelligence algorithms

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2020年第14期234卷 2100-2113页

作者： Deshamukhya, Tuhin Bhanja, Dipankar Nath, Sujit Natl Inst Technol Silchar Dept Mech Engn Silchar 788010 Assam India

Constructal T-shaped porous fins transfer better heat compared to the rectangular counterparts by improving the heat flow through the low resistive links. This type of fins can be used in aerospace engines which demand faster removal of heat without adding extra weight of the overall assembly. Here, in this study, three powerful nature-inspired metaheuristic algorithms such as particle swarm optimization, gravitational search algorithm, and Firefly algorithm have been used to optimize the dominant thermo physical as well as geometric parameters which are responsible for transferring heat at faster rates from the fin body satisfying a volume constraint. The temperature distribution along the stem and the flange has been plotted, and the effect of important parameters on the efficiency has been determined. Three different volumes are selected for the analysis, and the results have shown marked improvement in the optimized heat transfer rate. Particle swarm optimization has reported an increase of 0.81%, while Firefly algorithm reports 0.83% improvement as we increase the fin volume from 500 to 1000 and 0.19% (by PSO) and 0.4% (by FA) as the volume increases from 1000 to 1500. The paper also presents a scheme of reducing the computational effort required by the algorithms to converge around the optimum point. While a reduction of 14.36% computational effort has been achieved in particle swarm optimization's convergence time, Firefly algorithm took 24.64% less time to converge at the near-optimum point. While particle swarm optimization has converged at better optimal points compared to Firefly algorithm and gravitational search algorithm, gravitational search algorithm has outperformed the two algorithms in terms of computational time. gravitational search algorithm took 61.72 and 29.33% less time to converge as compared to particle swarm optimization and Firefly algorithm, respectively.

关键词： Constructal T-shaped porous fins particle swarm optimization firefly optimization gravitational search algorithm Swarm Intelligence

来源：评论

学校读者我要写书评

暂无评论

Why 'GSA: a gravitational search algorithm' is not genuinely based on the law of gravity

引用

NATURAL COMPUTING 2012年第4期11卷 719-720页

作者： Gauci, Melvin Dodd, Tony J. Gross, Roderich Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

This letter highlights a fundamental inconsistency in the formulation of the gravitational search algorithm (GSA) (Rashedi et al., Inf Sci 2232-48, 2009). GSA is said to be based on the law of gravity, that is, candidate solutions attract each other in the search space based on their relative distances and 'masses' (qualities). We show that, contrary to what is claimed, GSA does not take the distances between solutions into account, and therefore cannot be considered to be based on the law of gravity.

关键词： gravitational search algorithm Heuristic search algorithms Law of gravity Natural computing Optimization

来源：评论

学校读者我要写书评

暂无评论

Brain Image Segmentation Based on the Hybrid of Back Propagation Neural Network and AdaBoost System

引用

JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2020年第3期92卷 289-298页

作者： Chao, Zhen Kim, Hee-Joung Yonsei Univ Coll Hlth Sci Dept Radiat Convergence Engn 1 Yonseidae Gil Wonju 26493 Gangwon South Korea Yonsei Univ Coll Hlth Sci Dept Radiol Sci 1 Yonseidae Gil Wonju 26493 Gangwon South Korea

The segmentation of brain magnetic resonance (MR) images can provide more detailed anatomical information, which can be of great help for the proper diagnosis of brain diseases. Therefore, the study of medical image segmentation technology is crucial and necessary. Owing to the presence of equipment noise and the complexity of the brain structure, the existing methods have various shortcomings and their performances are not ideal. In this study, we propose a new method based on back propagation (BP) neural networks and the AdaBoost algorithm. The BP neural network that we created has a 1-7-1 structure. We trained the system using a gravitational search algorithm. (In this algorithm, we use segmented images, which were obtained by state-of-the-art methods, as ideal output data.) Based on this, we established and trained 10 groups of back propagation neural networks (BPNNs) by applying 10 groups of different data. Subsequently, we adopted the AdaBoost algorithm to obtain the weight of each BPNN. Finally, we updated the BPNNs by training the gravitational search and AdaBoost algorithms. In this experiment, we used one group of brain magnetic resonance imaging (MRI) datasets. A comparison with four state-of-the-art segmentation methods through subjective observation and objective evaluation indexes reveals that the proposed method achieved better results for brain MR image segmentation.

关键词： Medical image segmentation Back propagation neural network AdaBoost algorithm gravitational search algorithm

来源：评论

学校读者我要写书评

暂无评论

Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm

引用

APPLIED MATHEMATICS AND COMPUTATION 2012年第22期218卷 11125-11137页

作者： Mirjalili, SeyedAli Hashim, Siti Zaiton Mohd Sardroudi, Hossein Moradian Univ Teknol Malaysia Fac Comp Sci & Informat Syst Soft Comp Res Grp Utm Skudai 81310 Johor Malaysia

The gravitational search algorithm (GSA) is a novel heuristic optimization method based on the law of gravity and mass interactions. It has been proven that this algorithm has good ability to search for the global optimum, but it suffers from slow searching speed in the last iterations. This work proposes a hybrid of Particle Swarm Optimization (PSO) and GSA to resolve the aforementioned problem. In this paper, GSA and PSOGSA are employed as new training methods for Feedforward Neural Networks (FNNs) in order to investigate the efficiencies of these algorithms in reducing the problems of trapping in local minima and the slow convergence rate of current evolutionary learning algorithms. The results are compared with a standard PSO-based learning algorithm for FNNs. The resulting accuracy of FNNs trained with PSO, GSA, and PSOGSA is also investigated. The experimental results show that PSOGSA outperforms both PSO and GSA for training FNNs in terms of converging speed and avoiding local minima. It is also proven that an FNN trained with PSOGSA has better accuracy than one trained with GSA. (c) 2012 Elsevier Inc. All rights reserved.

关键词： FNN Neural network Learning neural network gravitational search algorithm Particle swarm optimization PSO Evolutionary algorithm Multilayer perceptron

来源：评论

学校读者我要写书评

暂无评论

A maiden application of gravitational search algorithm with wavelet mutation for the solution of economic load dispatch problems

引用

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2012年第1期4卷 33-46页

作者： Chatterjee, A. Ghoshal, S. P. Mukherjee, V. Indian Sch Mines Dept Elect Engn Dhanbad 826004 Jharkhand India Natl Inst Technol Dept Elect Engn Durgapur W Bengal India Asansol Engn Coll Dept Elect Engn Asansol W Bengal India

gravitational search algorithm (GSA) is one of the new optimisation algorithms based on the law of gravity and mass interactions. In this algorithm, the searcher agents are a collection of masses, and their interactions are based on the Newtonian laws of gravity and motion. In this article, a novel GSA with wavelet mutation (WM) (GSAWM) is proposed. It utilises the wavelet theory to enhance the GSA in exploring the solution space more effectively for a better solution. This algorithm is utilised for the optimal solutions of different economic load dispatch (ELD) problems of power systems. The obtained results are compared with those of the other state-of-the-art heuristic optimisation techniques published in the literature. Both the near-optimality of the solution and the convergence speed of the algorithm are promising.

关键词： economic dispatch gravitational search algorithm GSA prohibited operating zones ramp rate limits valve point effects wavelet mutation WM

来源：评论

学校读者我要写书评

暂无评论

A prototype classifier based on gravitational search algorithm

引用

APPLIED SOFT COMPUTING 2012年第2期12卷 819-825页

作者： Bahrololoum, Abbas Nezamabadi-Pour, Hossein Bahrololoum, Hamid Saeed, Masoud Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran

In recent years, heuristic algorithms have been successfully applied to solve clustering and classification problems. In this paper, gravitational search algorithm (GSA) which is one of the newest swarm based heuristic algorithms is used to provide a prototype classifier to face the classification of instances in multi-class data sets. The proposed method employs GSA as a global searcher to find the best positions of the representatives (prototypes). The proposed GSA-based classifier is used for data classification of some of the well-known benchmark sets. Its performance is compared with the artificial bee colony (ABC), the particle swarm optimization (PSO), and nine other classifiers from the literature. The experimental results of twelve data sets from UCI machine learning repository confirm that the GSA can successfully be applied as a classifier to classification problems. (C) 2011 Elsevier B. V. All rights reserved.

关键词： Classification Prototype classifier Swarm intelligence gravitational search algorithm UCI machine learning repository

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：