检索结果-内蒙古大学图书馆

A modified gravitational search algorithm for slope stability analysis

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2012年第8期25卷 1589-1597页

作者： Khajehzadeh, Mohammad Taha, Mohd Raihan El-Shafie, Ahmed Eslami, Mahdiyeh Univ Kebangsaan Malaysia Dept Civil & Struct Engn Bangi 43600 Selangor Malaysia Univ Kebangsaan Malaysia Dept Elect Engn Bangi 43600 Selangor Malaysia

This paper first proposes an effective modification for the gravitational search algorithm. The new strategy used an adaptive maximum velocity constraint, which aims to control the global exploration ability of the original algorithm, increase its convergence rate and thereby to obtain an acceptable solution with a lower number of iterations. We testify the performance of the modified gravitational search algorithm (MGSA) on a suite of five well-known benchmark functions and provide comparisons with standard gravitational search algorithm (SGSA). The simulated results illustrate that the modified GSA has the potential to converge faster, while improving the quality of solution. Thereafter, the proposed MGSA is employed to search for the minimum factor of safety and minimum reliability index in both deterministic and probabilistic slope stability analysis. The factor of safety is formulated using a concise approach of the Morgenstern and Price method and the advanced first-order second-moment (AFOSM) method is adopted as the reliability assessment model. The numerical experiments demonstrate that the modified algorithm significantly outperforms the original algorithm and some other methods in the literature. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： gravitational search algorithm Adaptive maximum velocity Minimum factor of safety Minimum reliability index

来源：评论

学校读者我要写书评

暂无评论

Enhancing hosting capacity of intermittent wind turbine systems using bi-level optimisation considering OLTC and electric vehicle charging stations

引用

IET RENEWABLE POWER GENERATION 2020年第17期14卷 3558-3567页

作者： Ali, Abdelfatah Mahmoud, Karar Lehtonen, Matti South Valley Univ Dept Elect Engn Qena 83523 Egypt Aalto Univ Dept Elect Engn & Automat FI-00076 Espoo Finland Aswan Univ Dept Elect Engn Aswan 81542 Egypt

Worldwide, the hosting capacity of renewable energy sources (RES) is remarkably expanded in distribution systems. One of the most auspicious RES is wind turbine systems (WTSs), which can improve the performance of distribution systems. In turn, the integration of high WTS penetrations can also deviate the system operation away from the standard condition. To tackle this issue, we propose a method for enhancing the hosting capacity of multiple WTSs considering their intermittent generations in distribution systems. The proposed method considers the operation of the on-load tap changer (OLTC), allowing to solve voltage problems efficiently. Especially, the proposed method optimises the charging/discharging power of electric vehicles (EVs), which can contribute positively to regulating WTS intermittent generation. Additionally, the reactive power support of WTSs, complying with the IEEE 1547:2018 standard, is incorporated in the planning model of WTSs. To solve such an optimisation problem, a bi-level optimisation algorithm is developed based on the gravitational search algorithm. Comprehensive simulation results are performed on the 69-bus distribution feeder interconnected to four EV stations. Based on the results, the proposed approach can efficiently enhance/increase the hosting capacity of WTSs in distribution systems, thanks to the consideration of OLTC, reactive power support of WTSs and EVs.

关键词： search problems IEEE standards distributed power generation wind turbines on load tap changers optimisation reactive power wind power electric vehicle charging distribution networks hosting capacity intermittent wind turbine systems OLTC electric vehicle distribution systems system operation WTS intermittent generation reactive power support bilevel optimisation algorithm 69-bus distribution feeder renewable energy sources RES performance improvement on-load tap changer IEEE 1547:2018 standard planning model gravitational search algorithm

来源：评论

学校读者我要写书评

暂无评论

Hybrid frameworks for flexible job shop scheduling

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2020年第5-6期108卷 1563-1585页

作者： Bharti, Puja Jain, Sushma Thapar Univ CSED Patiala Punjab India

Effective scheduling is essential for manufacturing firms to survive in today's fiercely competitive marketplace. Improvisation of schedule by simultaneous optimization of the performance measures is imperative for the manufacturing firms to stay ahead of competition and is one of the key responsibilities of shop floor managers. The current study addresses flexible job shop scheduling problem (FJSSP), considering three objectives: makespan (MS), maximal machine workload (MW), and total workload (TW). This research approaches the problem by developing two hybrid frameworks. The first framework (Hybrid-I) is a co-evolutionary combination of the social thinking capability of particle swarm optimization and local search capability of gravitational search algorithm. The second approach (Hybrid-II), hybridize the effectiveness of genetic algorithm in finding global best region with PSO's cluster interactions to improve the search for an optimal solution. A well-designed, efficient version of PSO (ePSO), that inherits twofold improvement through variable random function strategy and mutation strategy, is applied in both the approaches. With the view to make a reasonable comparison between both approaches and with the state-of-the-art methods, tests have been conducted on 28 benchmark instances taken from three different data sets. Further, the managerial implication of this research has been validated by implementing Hybrid-II for an industrial case. The substantial performance of the proposed approach on benchmark instances as well as real-life industrial data supports a strong candidature for optimization of FJSSP.

关键词： Flexible job shop scheduling Hybrid Particle swarm optimization gravitational search algorithm Genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

A network traffic prediction model of smart substation based on IGSA-WNN

引用

ETRI JOURNAL 2020年第3期42卷 366-375页

作者： Xia, Xin Liu, Xiaofeng Lou, Jichao Wuhan Inst Shipbldg Technol Coll Comp Informat Technol Wuhan Peoples R China Wenzhou Vocat & Tech Coll Dept Informat Technol Wenzhou Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China

The network traffic prediction of a smart substation is key in strengthening its system security protection. To improve the performance of its traffic prediction, in this paper, we propose an improved gravitational search algorithm (IGSA), then introduce the IGSA into a wavelet neural network (WNN), iteratively optimize the initial connection weighting, scalability factor, and shift factor, and establish a smart substation network traffic prediction model based on the IGSA-WNN. A comparative analysis of the experimental results shows that the performance of the IGSA-WNN-based prediction model further improves the convergence velocity and prediction accuracy, and that the proposed model solves the deficiency issues of the original WNN, such as slow convergence velocity and ease of falling into a locally optimal solution;thus, it is a better smart substation network traffic prediction model.

关键词： analysis gravitational search algorithm modeling network traffic prediction smart substation traffic measurement visualization wavelet neural network

来源：评论

学校读者我要写书评

暂无评论

Research on Remaining Useful Life Prognostics Based on Fuzzy Evaluation-Gaussian Process Regression Method

引用

IEEE ACCESS 2020年 8卷 71965-71973页

作者： Kang, Weijie Xiao, Jiyang Xiao, Mingqing Hu, Yangguang Zhu, Haizhen Li, Jianfeng Air Force Engn Univ Sch Aeronaut Engn Xian 710051 Peoples R China

To achieve efficient and accurate remaining life prediction and effectively express the uncertainty of prediction results, this paper proposes a remaining life prediction method based on fuzzy evaluation-Gaussian process regression (FE-GPR). First, the prediction of the remaining useful life (RUL) is affected by unknown variables, such as the environment, and it is difficult to achieve accurate predictions. It is necessary to effectively express the uncertainty of such prediction results. In this paper, we have put forward a RUL prediction method based on GPR, which can realize the RUL prediction with a confidence interval. Second, combined with the characteristics of the GPR method, an observation data preprocessing method based on fuzzy evaluation is proposed. The initial fuzzy evaluation method is established based on expert knowledge. Then, the classification nodes are optimized by the gravitational search algorithm (GSA) and historical data. This method, which uses fuzzy logic combined with expert knowledge, can avoid over-fitting in the case of limited data, and effectively improves the prediction accuracy of the GPR model. Finally, we use NASA PCoE. lithium battery data for a case study. The results show that the FE-GPR method achieves a more accurate RUL prediction and effectively reflects the uncertainty of the prediction results.

关键词： Ground penetrating radar Predictive models Uncertainty Data models Feature extraction Gaussian processes Fuzzy evaluation gaussian process regression remaining life prediction gravitational search algorithm

来源：评论

学校读者我要写书评

暂无评论

MOGSABAT: a metaheuristic hybrid algorithm for solving multi-objective optimisation problems

引用

NEURAL COMPUTING & APPLICATIONS 2020年第8期32卷 3101-3115页

作者： Tariq, Iraq AlSattar, H. A. Zaidan, A. A. Zaidan, B. B. Abu Bakar, M. R. Mohammed, R. T. Albahri, O. S. Alsalem, M. A. Albahri, A. S. Univ Baghdad Fac Sci Dept Math Baghdad Iraq Univ Putra Malaysia Dept Math FS Seri Kembangan Malaysia Univ Pendidikan Sultan Idris Dept Comp FSKIK Tanjong Malin Malaysia Univ Putra Malaysia Dept Comp Sci FSKTM Seri Kembangan Malaysia

This study proposes a novel strength of multi-objective gravitational search algorithm and bat algorithm MOGSABAT to solve multi-objective optimisation problem. The proposed MOGSABAT algorithm is divided into three stages. In the first stage (moving space), a switch in a solution from single function to multiple functions that contain more than one objective to use the gravitational search algorithm GSA is determined. We established a new equation to calculate the masses of individuals in the population using the theoretical work found in the strength Pareto evolutionary algorithm. In the second stage (moving in space), how to handle the bat algorithm BAT to solve multiple functions is established. We applied the theoretical work of multi-objective particle swarm optimisation into the BAT algorithm to solve multiple functions. In the third stage, multi-objective GSA and multi-objective BAT are integrated to obtain the hybrid MOGSABAT algorithm. MOGSABAT is tested by adopting a three-part evaluation methodology that (1) describes the benchmarking of the optimisation problem (bi-objective and tri-objective) to evaluate the performance of the algorithm;(2) compares the performance of the algorithm with that of other intelligent computation techniques and parameter settings;and (3) evaluates the algorithm based on mean, standard deviation and Wilcoxon signed-rank test statistic of the function values. The optimisation results and discussion confirm that the MOGSABAT algorithm competes well with advanced metaheuristic algorithms and conventional methods.

关键词： Multi-objective optimisation problem gravitational search algorithm Bat algorithm Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

Development of fuzzy-GMDH model optimized by GSA to predict rock tensile strength based on experimental datasets

引用

NEURAL COMPUTING & APPLICATIONS 2020年第17期32卷 14047-14067页

作者： Harandizadeh, Hooman Armaghani, Danial Jahed Mohamad, Edy Tonnizam Shahid Bahonar Univ Kerman Dept Civil Engn Fac Engn Pajoohesh SqImam Khomeni HighwayPOB 76169133 Kerman Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Univ Teknol Malaysia Sch Civil Engn Ctr Trop Geoengn GEOTROPIK Fac Engn Johor Baharu 81310 Malaysia

The tensile strength (TS) of the rock is one the most key parameters in designing process of foundations and tunnels structures. However, direct techniques for TS determination (laboratory investigations) are not efficient with respect to cost and time. This investigation attempts to develop an innovative hybrid intelligent model, i.e. fuzzy-group method of data handling (GMDH) optimized by the gravitational search algorithm (GSA), fuzzy-GMDH-GSA, for prediction of the rock TS. To establish a database, the rock samples collected from a tunnel site were evaluated in the laboratory and a database (with the Schmidt hammer test, dry density test, and point load test as inputs and Brazilian tensile strength, BTS, as output) was prepared for modelling. Then, a fuzzy-GMDH-GSA model was developed to predict BTS of the rock considering the most influential of this predictive model. In addition, a fuzzy model as well as a GMDH model were constructed to predict BTS for comparison purposes. The performances of the proposed predictive models were evaluated by comparing the values of several statistical metrics such as correlation coefficient (R). R values of 0.90, 0.86, and 0.86 were obtained for testing datasets of fuzzy-GMDH-GSA, GMDH, and fuzzy models, respectively, which show that the fuzzy-GMDH-GSA predictive model is able to deliver greater prediction performance compared to other constructed models. The results confirmed the effective role of the GSA, as a powerful optimization algorithm in efficiency of hybrid fuzzy-GMDH-GSA model. Moreover, results of sensitivity analysis showed that the point load index is the most effective input on output of this study.

关键词： Tensile strength gravitational search algorithm Optimization algorithm Group method of data handling Fuzzy system

来源：评论

学校读者我要写书评

暂无评论

Combined economic and emission dispatch solution using gravitational search algorithm

引用

SCIENTIA IRANICA 2012年第6期19卷 1754-1762页

作者： Guvenc, U. Sonmez, Y. Duman, S. Yorukeren, N. Duzce Univ Fac Technol Dept Elect Elect Engn Duzce Turkey Gazi Univ Gazi Vocat Coll Dept Elect & Energy Technol Ankara Turkey Duzce Univ Tech Educ Fac Dept Elect Educ Duzce Turkey Kocaeli Univ Fac Engn Dept Elect Engn Kocaeli Turkey

In this article, the gravitational search algorithm (GSA) has been proposed to find the optimal solution for Combined Economic and Emission Dispatch (CEED) problems. It is aimed, in the CEED problem, that scheduling of generators should operate with both minimum fuel costs and emission levels, simultaneously, while satisfying the load demand and operational constraints. In this paper, the CEED problem is formulated as a multi-objective problem by considering the fuel cost and emission objectives of generating units. The bi-objective optimization problem is converted into a single objective function using a price penalty factor in order to solve it with GSA. The proposed algorithm has been implemented on four different test cases, having a valve point effect with transmission loss and having no valve point effect without transmission loss. In order to see the effectiveness of the proposed algorithm, it has been compared with other algorithms in the literature. Results show that the GSA is more powerful than other algorithms. (C) 2012 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.

关键词： Combined economic and emission dispatch gravitational search algorithm Power systems Optimization

来源：评论

学校读者我要写书评

暂无评论

Decision function estimation using intelligent gravitational search algorithm

引用

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2012年第2期3卷 163-172页

作者： Askari, Hossein Zahiri, Seyed-Hamid Birjand Univ Fac Engn Dept Elect Engn Birjand Iran

There are various kinds of supervised classification techniques such as Bayesian classifier, k nearest neighbor, neural network and rule based classifiers. A kind of supervised classifier, estimates the necessary decision hyperplanes for separating the feature space to distinct regions for recognizing unknown put patterns. In this paper a novel swarm intelligence based classifier is described for decision function estimation without requirement to priory knowledge. The utilized swarm intelligence technique is gravitational search algorithm (GSA) which has been recently reported. The proposed method is called intelligent GSA based classifier (IGSA-classifier). At first, a fuzzy system is designed for intelligently updating the effective parameters of GSA. Those are gravitational coefficient and the number of effective objects, two important parameters which play major roles on search process of GSA. Then the designed intelligent GSA is employed to construct a novel decision function estimation algorithm from feature space. Extensive experimental results on different benchmarks and a practical pattern recognition problem with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the capability of the proposed method. The comparative results show that the performance of the proposed classifier is comparable to or better than the performance of other swarm intelligence based and evolutionary classifiers.

关键词： gravitational search algorithm Fuzzy system Decision function Classifier

来源：评论

学校读者我要写书评

暂无评论

Optimal design and implementation of compact fractional order microwave integrator

引用

INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2020年第8期30卷 e22260-e22260页

作者： Gautam, Usha Rawat, Tarun K. Aggarwal, Apoorva Netaji Subhas Inst Technol Dept Elect & Commun Engn New Delhi India Bharati Vidyapeeths Coll Engn Dept Elect & Elect Engn New Delhi India

The implementation of novel, stable, accurate, and wideband infinite impulse response fractional order microwave integrators (FOMIs) is presented. The formulation of FOMIs is employed with equal length line elements in cascading. The optimum values of characteristic impedances of the line elements are determined by approximation to the ideal fractional order integrator (FOI). The hybrid algorithm (HPSO-GSA) combining particle swarm optimization (PSO) and gravitational search algorithm (GSA) which integrates PSO's exploitation and GSA's exploration ability is used. The comparison of HPSO-GSA with PSO and GSA is carried out for the proposed FOMIs. The performance criteria used are magnitude response, absolute magnitude error, phase response, pole-zero response, percentage improvement graph, and convergence rate. The simulation analysis affirms the superiority of proposed FOMI using HPSO-GSA. The absolute magnitude error of proposed 0.5 order HPSO-GSA-based FOMI is as low as 0.9436. The structure of the designed FOI is implemented with microstrip configuration on RT/Duroid substrate with permittivity 2.2 and thickness 0.762 mm that is eligible for wideband microwave integrator. The designed FOMI is compact in size and suitable to cover microwave applications. The measured results are established in fine agreement with simulation results in the frequency range of 2-9 GHz in MATLAB and Advanced Design Software environment.

关键词： fractional order gravitational search algorithm hybrid PSO-GSA hybridization IIR filter microwave integrator particle swarm optimization

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：