检索结果-内蒙古大学图书馆

International Conference on Advances in Computing (ICAdC 2012)

作者： Naik, Anima Parvathi, K. Satapathy, Suresh Chandra Nayak, Ramanuja Panda, B. S. MITS Rayagada India

ISBN: (纸本)9788132207399;9788132207405

The QoS multicast routing problem is to find a multicast routing tree with minimal cost that can satisfy constraints such as bandwidth, delay. This problem is NP Complete. Hence, the problem is usually solved by heuristic or intelligence optimization. In this paper, we present a teaching learning based optimization method to optimize the multicast tree. A fitness function is used to implement the constraints specified by the Quality of Service conditions. The experimental results dealt with relations between the number of nodes, edges in the input graph and convergence time, the optimal solution quality comparison with other evolutionary techniques. The results reveal that our algorithm performs better than the existing algorithms.

关键词： Multicast QoS routing teaching learning based optimization fitness function

来源：评论

学校读者我要写书评

暂无评论

Multiuser Detection over Generalized-K Fading Channels using Two-Stage Initialized teaching learning based optimization 5

Multiuser Detection over Generalized-<i>K</i> Fading Channel...

引用

International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Gondela, Lakshmi Manasa Pamula, Vinay Kumar Tipparti, Anil Kumar Univ Coll Engn Kakinada JNTUK Dept Elect & Commun Engn Kakinada 533003 India MVSR Engn Coll Dept Elect & Commun Engn Hyderabad 501510 Andhra Pradesh India

ISBN: (纸本)9781509020287

A multiuser detection (MUD) technique for direct sequence-code division multiple access (DS-CDMA) systems over generalized-K (GK) fading channels using teaching learning based optimization algorithm (TLBO) with two-stage initialization (TSI) is proposed. In DS-CDMA systems, MUD techniques are applied to combat multiple access interference (MAI)and ambient noise. Empirical results proved that the ambient noise is non-Gaussian and impulse in nature which degrades the performance of the system substantially. The DS-CDMA signals are transmitted over channels that introduce impulsive noise, shadowing and fading. In this paper, we develop least-squares (LS), Huber and Hampel M-estimation based MUD technique for joint detection of DS-CDMA signals in the presence of MAI, impulsive noise, modeled by Laplace distribution, and channel fading, modeled by GK distribution. The TLBO with TSI (TLBO-TSI) algorithm is used to minimize a penalty function that is a less rapidly increasing function of residuals. Average bit error rate (BER) is computed to assess the performance of the TLBO-TSI based detector. Obtained results demonstrate that the proposed robust technique offer significant performance gains with increase in signal-to-noise ratio (SNR) and diversity order in the presence of heavy-tailed impulsive noise.

关键词： Bit error rate Direct sequence-code division multiple access Generalized-K distribution multi user detection teaching learning based optimization

来源：评论

学校读者我要写书评

暂无评论

Power Quality Improvement with Shunt Active Filter under Various Mains Voltage Using teaching learning based optimization 2

Power Quality Improvement with Shunt Active Filter under Var...

引用

2nd International Conference on Recent Advances in Engineering

作者： Mishra, Soumya Ray, Pravat Kumar Natl Inst Technol Dept Elect Engn Rourkela 769008 India

ISBN: (纸本)9781467382533

In this paper, instantaneous reactive power theory based a modified control algorithm is implemented for 3-phase 3-wire shunt active power filter to suppress harmonics and reactive power under non ideal source voltage. The reference current signals are generated by sensing the source voltage, load current and dc bus voltage. With these signals the gate driving pulses are generated via hysteresis band current controller. To check robustness of the control algorithm four different cases such as unbalanced, balanced distorted, unbalanced distorted main voltage and faults on load are considered. For more improvement in the performance of active filter, the Active filter is optimized with teaching-learning-based optimization. The simulation work is carried out in MA TLAB/SIMULINK Power system block set. The performance of the implemented algorithm with teaching-learning-based optimization is found quite effective and satisfactory under these conditions.

关键词： Shunt active power filter teaching learning based optimization Total harmonic distortion Instantaneous power theory

来源：评论

学校读者我要写书评

暂无评论

Comparative Performance Evaluation of teaching learning based optimization against Genetic Algorithm on Benchmark Functions

Comparative Performance Evaluation of Teaching Learning Base...

引用

IEEE Power, Communication and Information Technology Conference (PCITC)

作者： Ebraheem, M. Jyothsna, T. R. GITAM Univ GITAM Inst Technol Dept Elect & Elect Engn Visakhapatnam Andhra Pradesh India AU Coll Engn Dept Elect Engn Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9781479974559

In this paper effectiveness of newly introduced 'teaching learning based optimization' (TLBO) is evaluated against different benchmark optimization problems. The effectiveness, then, is compared with the performance of genetic algorithm (GA) using the same parameters as used with TLBO. The functions on which the two algorithms applied in this work are rastrigin function, quartic, rosenbrock, six hump camel back. And the results are validated. Both the algorithms are applied on these functions using the same software (MATLAB) on the same platform also with the same number of population and elite group. The effectiveness of the TLBO is compared with that of GA based on the speed, average solution.

关键词： teaching learning based optimization Genetic Algorithm Benchmark functions

来源：评论

学校读者我要写书评

暂无评论

Binary Classification of Static Security Assessment using teaching learning based optimization enhanced Support Vector Machine 16

Binary Classification of Static Security Assessment using Te...

引用

16th IEEE-India-Council International Conference (INDICON)

作者： Dhandhia, Astik Pandya, Vivek Pandit Deendayal Petr Univ Elect Engn Dept Gandhinagar Gujarat India

ISBN: (纸本)9781728123271

Static security assessment is very essential for secure and reliable operation of power system. Lengthy calculation time and large memory requirement are the main drawbacks of the conventional power flow methods. The composite security index has been implemented to prevent the problem of masking associated with the performance index earlier used for static security assessment. Binary classifier for static security assessment of the power system has been developed using support vector machine. The parameters of Radial Basis Function are selected using teaching learning based optimization method. Sequential forward selection method has been used to select features. The results of the proposed teaching learning based optimization enhanced binary classifier are validated using two IEEE standard test systems.

关键词： static security feature selection grid search teaching learning based optimization composite security index

来源：评论

学校读者我要写书评

暂无评论

GPGPU based teaching learning based optimization and Artificial Bee Colony Algorithm for Unconstrained optimization Problems 5

GPGPU based Teaching Learning based Optimization and Artific...

引用

IEEE International Advance Computing Conference (IACC 2015)

作者： Mane, Sandeep U. Omane, Rajshree Pawar, Aprupa Rajarambapu Inst Technol Dept CSE Rajaramnagar India

ISBN: (纸本)9781479980475

teaching learning based optimization (TLBO) and Artificial bee colony (ABC) algorithm is population based modern method of optimization, used to solve diverse complex engineering and real time applications. To obtain best solution for the complex problem it requires more time and results in performance degradation. To improve the performance of the population based algorithm, they are either parallelized or implemented on General Purpose Graphic Processing Unit (GPGPU). In this paper, the GPGPU based implementation of TLBO and ABC algorithm is discussed to solve unconstrained benchmark problems. The performance of both the approaches is compared based on standard deviation, standard error mean and time. It is observed that both the approaches gives good results but time taken by TLBO algorithm is more as compared to ABC algorithm.

关键词： Artificial bee colony teaching learning based optimization Unconstrained optimization problem General Purpose Graphic Processing Unit

来源：评论

学校读者我要写书评

暂无评论

Optimal Location and Sizing of Distributed Generation Units for Maximum Loss Reduction using teaching learning based optimization through Matlab GUI

Optimal Location and Sizing of Distributed Generation Units ...

引用

International Conference on Electrical, Electronics, Signals, Communication and optimization (EESCO)

作者： Harini, M. Reddy, M. Damodar SV Univ Coll Engn EEE Dept Tirupati Andhra Pradesh India

ISBN: (纸本)9781479976782

Optimal placement of distributed generation in a distribution system is a problem which involves the optimization of both discrete and continuous variables. In radial distribution network, distributed generation (DG) plays a crucial role in satisfying the demands of electrical energy. It is a small generating plant which supports the distribution network by minimizing system power losses, improving the stability and voltage of the network. In this paper, teaching learning based optimization (TLBO) technique is used for finding optimal DG locations and sizes simultaneously. The main goal is to achieve substantial improvement in the voltage profile and significant reduction in network power losses. The TLBO is inspired by the influence of the teaching by the teacher on the students in a class. In this paper, GUI is developed in Matlab to perform load flow and to also obtain optimal locations and capacities of DG units. The proposed method was tested on IEEE 15 bus, 33 bus and 69 bus systems and the results were discussed.

关键词： Distributed Generation teaching learning based optimization Voltage Profile Loss Reduction Graphical User Interface

来源：评论

学校读者我要写书评

暂无评论

Application of teaching learning based optimization in Long term Optimal Reservoir Operation

Application of Teaching Learning Based Optimization in Long ...

引用

10th International Conference on Modelling, Identification and Control (ICMIC)

作者： You Wenxia Qiu Tianyi Li Wenwu Zhang Zhi China Three Gorges Univ Coll Elect Engn & New Energy Yichang Peoples R China Guangdong Power Grid Corp Co Ltd Zhongshan Elect Power Supply Bur Guangzhou Guangdong Peoples R China State Gird DC Power Construct Branch Co Shanghai Peoples R China

ISBN: (纸本)9781538654163

Long term optimal reservoir operation is a nonlinear optimization problem. This paper adopts teaching learning based optimization (TLBO) to solve the Long term scheduling for reservoirs. Firstly, the basic test function is used to verify that the TLBO algorithm has high accuracy. Then the TLBO algorithm is applied to the Long term optimal reservoir operation model and the competitive selection and semi-feasible domain are introduced to deal with the constraint conditions. Comparing the TLBO algorithm with the genetic algorithm and the dynamic programming algorithm, solving the available TLBO algorithm has the advantages of high calculation accuracy, short computation time.

关键词： teaching learning based optimization Long term optimal reservoir operation competition selection semi - feasible domain

来源：评论

学校读者我要写书评

暂无评论

Finding Patterns in Protein Sequences by Using a Hybrid Multiobjective teaching learning based optimization Algorithm

引用

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015年第3期12卷 656-666页

作者： Gonzalez-Alvarez, David L. Vega-Rodriguez, Miguel A. Rubio-Largo, Alvaro Univ Extremadura Escuela Politecn Dept Comp & Commun Technol Caceres 10003 Spain

Proteins are molecules that form the mass of living beings. These proteins exist in dissociated forms like amino-acids and carry out various biological functions, in fact, almost all body reactions occur with the participation of proteins. This is one of the reasons why the analysis of proteins has become a major issue in biology. In a more concrete way, the identification of conserved patterns in a set of related protein sequences can provide relevant biological information about these protein functions. In this paper, we present a novel algorithm based on teaching learning based optimization (TLBO) combined with a local search function specialized to predict common patterns in sets of protein sequences. This population-based evolutionary algorithm defines a group of individuals (solutions) that enhance their knowledge (quality) by means of different learning stages. Thus, if we correctly adapt it to the biological context of the mentioned problem, we can get an acceptable set of quality solutions. To evaluate the performance of the proposed technique, we have used six instances composed of different related protein sequences obtained from the PROSITE database. As we will see, the designed approach makes good predictions and improves the quality of the solutions found by other well-known biological tools.

关键词： teaching learning based optimization hybrid algorithm multiobjective optimization proteins PROSITE

来源：评论

学校读者我要写书评

暂无评论

Parallel computing strategies for Sanitized teaching learning based optimization

引用

JOURNAL OF COMPUTATIONAL SCIENCE 2022年 63卷

作者： Kommadath, Remya Maharana, Debasis Sivadurgaprasad, Chinta Kotecha, Prakash Indian Inst Technol Guwahati Dept Chem Engn Gauhati 781039 Assam India Indian Inst Technol Guwahati Sch Business Gauhati 781039 Assam India

teaching-learning-based optimization is a metaheuristic technique that imitates the knowledge transfer in a classroom. The competitive performance with other state-of-the-art metaheuristic algorithms, innate simplicity, and the requirement of only two user-defined parameters are the primary reason behind the popularity of this algorithm. However, due to the inherent nature of the teaching learning based optimization, it is not amenable to parallel computing for the multiple evaluations of the fitness function, thereby limiting its application to computationally intensive problems. This article considers the teaching learning based optimization algorithm without the duplicate removal step as Sanitized teaching learning based optimization. In this work, three competitive parallelized strategies of Sanitized teaching learning based optimization are suggested that can harness the benefits of parallel computing. The efficacies of the proposed strategies are demonstrated on 14 benchmark problems and the CEC'14 real parameter benchmark test suite. Additionally, the impact on the computational time with respect to the computational complexity of the objective function and the number of resources has been studied. It is observed that the proposed strategies can lead up to a 90% reduction in computational time over Sanitized teaching learning based optimization algorithm.

关键词： Metaheuristic techniques teaching learning based optimization Sanitized teaching learning based optimization Parallel computing

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：