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teaching learning based optimization for economic load dispatch problem considering valve point loading effect

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 73卷 456-464页

作者： Banerjee, Sumit Maity, Deblina Chanda, Chandan Kumar Dr BC Roy Engn Coll Dept Elect Engn Durgapur 713206 India Netaji Subhash Engn Coll Dept Elect Engn Kolkata 700152 India Indian Inst Engn Sci & Technol Dept Elect Engn Sibpur 711103 Howrah India

This paper presents a novel teaching learning based optimization (TLBO) technique to solve economic load dispatch (ELD) of the thermal unit without considering transmission losses. The proposed methodology can take care of ELD considering nonlinearity such as valve point loading. The objective of economic load dispatch is to determine the optimal power generation of the units to meet the load demand, such that the overall cost of generation is minimized, while satisfying different operational constraints. TLBO is a recently developed evolutionary algorithm based on two basic concepts of education namely teaching phase and learning phase. At first, learners improve their knowledge through the teaching methodology of teacher and finally learners increase their knowledge by interactions among themselves. The effectiveness of the proposed algorithm has been verified on three different test systems with equality and inequality constraints. Compared with the other existing techniques demonstrates the superiority of the proposed algorithm. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Economic load dispatch Genetic algorithm Particle swarm optimization teaching learning based optimization Valve point loading effect

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Empirical Study on Clustering based on Modified teaching learning based optimization 2

Empirical Study on Clustering Based on Modified Teaching Lea...

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2nd International Conference on Intelligent Computing, Communication & Convergence (ICCC)

作者： Panigrahi, Sushanta Kumar Pattnaik, Sabyasachi Intersci Inst Management & Technol Bhubaneswar Odisha India Fakir Mohan Univ Bhubaneswar Odisha India

In this Paper the focus is given on data clustering using Modified teaching-learning based optimization (MTLBO) a hybridization technique of TLBO. Unlike TLBO, this population based method works on the effect of influence of a teacher on learners to find the optimum solution and it has been used for clustering. The motivation behind the data clustering is to find inherent structure in the data items and grouping then on the basis of their mutual similarity. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with other population based methods and finally it is implemented on clustering using neural network in data mining. (C) 2016 The Authors. Published by Elsevier B.V. This is an open acess article under the CC BY-NC-ND license.

关键词： Artificial neural network evolutionary algorithm particle Swarm optimization teaching learning based optimization clustering

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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...

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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

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teaching learning based optimization (TLBO) based Improved Iris Recognition System

Teaching Learning Based Optimization (TLBO) Based Improved I...

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23rd International Conference on Systems Engineering

作者： Agrawal, Shikha Sharma, Shraddha Silakari, Sanjay Rajiv Gandhi Proudyogiki Vishwavidyalaya UIT Dept Comp Sci & Engn Bhopal MP India

ISBN: (纸本)9783319084213

optimization technique plays an major role in the iris system. The optimized feature gives an optimized template for matching process and these optimized template increases the identification rate of iris system. In this paper, we proposed teaching learning based optimization (TLBO) based iris recognition system in which feature extraction phase of iris recognition system is optimized by using TLBO. The process of feature extraction is performed by texture feature extraction Gabor wavelet transform technique. TLBO is than applied on these features. teaching learning based optimization algorithm acquired the feature of iris image as a student and generates the optimized feature template as a teacher. The process of optimization designed the fitness constraints function for the selection of feature in student to teacher The proposed algorithm compared with other iris recognition methods Standard Iris Recognition System and Genetic Algorithm optimized Iris Recognition System. Experimental results when applied to CASIA dataset shows superior performance with better recognition rate. © Springer International Publishing Switzerland 2015.

关键词： Feature Extraction Gabor Wavelet Transform Genetic Algorithm Hough Transform Iris Recognition teaching learning based optimization

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Empirical Study on Clustering based on Modified teaching learning based optimization

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Procedia Computer Science 2016年 92卷 442-449页

作者： Sushanta Kumar Panigrahi Sabyasachi Pattnaik Interscience Institute of Management & Technology Bhubaneswar Odisha India Fakir Mohan University Bhubaneswar Odisha India

In this Paper the focus is given on data clustering using Modified teaching–learning based optimization (MTLBO) a hybridization technique of TLBO. Unlike TLBO, this population based method works on the effect of influence of a teacher on learners to find the optimum solution and it has been used for clustering. The motivation behind the data clustering is to find inherent structure in the data items and grouping then on the basis of their mutual similarity. The effectiveness of the method is tested on many benchmark problems with different characteristics and the results are compared with other population based methods and finally it is implemented on clustering using neural network in data mining.

关键词： Artificial neural network evolutionary algorithm particle Swarm optimization teaching learning based optimization clustering

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Optimal PD-PID cascaded controller based Automatic Generation Control of a multisource interconnected power system tuned by teaching learning based optimization 7

Optimal PD-PID cascaded controller based Automatic Generatio...

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7th IEEE India International Conference on Power Electronics (IICPE)

作者： Debnath, Manoj Kumar Patel, Nimai Charan Mallick, Ranjan Kumar Siksha O Anusandhan Univ Dept Elect Engn Bhubaneswar Odisha India Siksha O Anusandhan Univ Dept EEE Bhubaneswar Odisha India

ISBN: (纸本)9781509045303

This research article explains about the implementation of a novel PD-PID cascaded controller for automatic generation control of a two area multi source interconnected power system where in each area three types of generating sources are incorporated namely gas, hydro and thermal unit. teaching learning based optimization technique is used for optimization of the scaling factors of the proposed PD-PID controllers when a sudden load disturbance of 0.01 p.u. is considered in area 1 and Integral time absolute error (ITAE) is chosen as an objective function. The superior performance of the proposed optimal controller is proved by comparing the dynamic response of the system with a recently published result of PID controller optimized by Differential Evolution (DE) technique [16]. The performance of the system is analyzed under two cases: (1) With AC tie-line only and (2) With AC-DC tie-line. The evaluation is carried on considering various time response specifications like settling time, peak undershoots and peak overshoots.

关键词： Automatic Generation Control Proportional Integral-Derivative controller teaching learning based optimization PD-PID cascaded controller Multi source power system

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Comparative performance analysis of teaching learning based optimization for automatic load frequency control of multi-source power systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2015年 66卷 67-77页

作者： Barisal, A. K. VSSUT Dept Elect Engn Burla Odisha India

This paper presents a new population based parameter free optimization algorithm as teaching learning based optimization (TLBO) and its application to automatic load frequency control (ALFC) of multi-source power system having thermal, hydro and gas power plants. The proposed method is based on the effect of the influence of teacher on the output of learners and the learners can enhance their knowledge by interactions among themselves in a class. In this extensive study, the algorithm is applied in multi area and multi-source realistic power system without and with DC link between two areas in order to tune the PID controller which is used for automatic generation control (AGC). The potential and effectiveness of the proposed algorithm is compared with that of differential evolution algorithm (DE) and optimal output feedback controller tuning performance for the same power systems. The dynamic performance of proposed controller is investigated by different cost functions like integral of absolute error (IAE), integral of squared error (ISE), integral of time weighted squared error (ITSE) and integral of time multiplied absolute error (ITAE) and the robustness of the optimized controller is verified by its response toward changing in load and system parameters. It is found that the dynamic performance of the proposed controller is better than that of recently published DE optimized controller and optimal output feedback controller and also the proposed system is more robust and stable to wide changes in system loading, parameters, size and locations of step load perturbation and different cost functions. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Automatic load frequency control Automatic generation control Dynamic performance Multi source power system HVDC link teaching learning based optimization

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Simultaneous allocation of distributed resources using improved teaching learning based optimization

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ENERGY CONVERSION AND MANAGEMENT 2015年 103卷 387-400页

作者： Kanwar, Neeraj Gupta, Nikhil Niazi, K. R. Swarnkar, Anil Malaviya Natl Inst Technol Dept Elect Engn Jaipur Rajasthan India Taibah Univ Fac Engn Dept Elect Engn Madinah Saudi Arabia

Active and reactive power flow in distribution networks can be effectively controlled by optimally placing distributed resources like shunt capacitors and distributed generators. This paper presents improved variant of teaching learning based optimization (TLBO) to efficiently and effectively deal with the problem of simultaneous allocation of these distributed resources in radial distribution networks while considering multi-level load scenario. Several algorithm specific modifications are suggested in the standard form of TLBO to cope against the intrinsic flaws of this technique. In addition, an intelligent search approach is proposed to restrict the problem search space without loss of diversity. This enhances the overall performance of the proposed method. The proposed method is investigated on IEEE 33-bus, 69-bus and 83-bus test distribution systems showing promising results. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Distributed resources Meta-heuristic technique Intelligent search Smart distribution systems teaching learning based optimization

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A teaching learning based optimization for truss structures with frequency constraints

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STRUCTURAL ENGINEERING AND MECHANICS 2015年第4期53卷 833-845页

作者： Dede, Tayfun Togan, Vedat Karadeniz Tech Univ Dept Civil Engn TR-61080 Trabzon Turkey

Natural frequencies of the structural systems should be far away from the excitation frequency in order to avoid or reduce the destructive effects of dynamic loads on structures. To accomplish this goal, a structural optimization on size and shape has been performed considering frequency constraints. Such an optimization problem has highly nonlinear property. Thus, the quality of the solution is not independent of the optimization technique to be applied. This study presents the performance evaluation of the recently proposed meta-heuristic algorithm called teaching learning based optimization (TLBO) as an optimization engine in the weight optimization of the truss structures under frequency constraints. Some examples regarding the optimization of trusses on shape and size with frequency constraints are solved. Also, the results obtained are tabulated for comparison. The results demonstrated that the performance of the TLBO is satisfactory. Additionally, TLBO is better than other methods in some cases.

关键词： teaching learning based optimization frequency constraints shape and size optimization trusses

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

Multiuser Detection over Generalized-K Fading Channels using...

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International Conference on Advances in Computing, Communications and Informatics

作者： Lakshmi Manasa Gondela Vinay Kumar Pamula Anil Kumar Tipparti Department of Electronics and Communication Engineering University College of Engineering Kakinada JNTUK Kakinada India 533003 Department of Electronics and Communication Engineering MVSR Engineering College Hyderabad India 501510

ISBN: (纸本)9781509020300

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

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