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Optimal generator rescheduling with distributed slack bus model for congestion management using improved teaching learning based optimization algorithm

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2018年第11期43卷 1-11页

作者： Suganthi, S. T. Devaraj, D. Thilagar, S. Hosimin Ramar, K. Sri Shakthi Inst Engn & Technol Dept EEE Coimbatore Tamil Nadu India Kalasalingam Univ Dept EEE Krishnan Koil India Anna Univ Dept EEE Madras Tamil Nadu India Multimedia Univ Fac Engn Cyberjaya Malaysia

This article proposes a new strategy for Congestion Management (CM) through Generation Rescheduling (GR) with Distributed Slack Bus (DSB) model using Improved teaching learning based optimization (ITLBO) algorithm. Most of the previous research mainly focuses on rescheduling the existing generators for CM and least concerned about the consequence of this rescheduling on the slack bus. As a result, the slack bus is supposed to carry the entire residual effect of all other generators which leads to increased Congestion Management Cost (CMC). This work proposes a distributed slack bus model for reducing the excess burden on the slack bus and a two-fold contribution is made to develop the proposed technique. First, is the selection of the participating generator for CM by means of Incremental Generator Sensitivity Factor (IGSF), which adopts the generator sensitivity as well as the bidding cost. Next, CM is formulated as an optimization problem with the objective function involving congestion management cost and solved using ITLBO algorithm, which incorporates the self-motivated learning concept with the basic TLBO operators. For evaluation, the standard IEEE 30-bus and IEEE 118-bus systems are used and the obtained results are compared with the other CM systems reported through literature.

关键词： Congestion management generation rescheduling generator sensitivity factor teaching learning based optimization restructured power systems

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An energy efficient teaching learning based optimization approach for common content distribution in mobile ad hoc networks

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COMPUTERS & ELECTRICAL ENGINEERING 2018年 72卷 296-306页

作者： Maiti, Mahendra Mukherjee, Srijit Thakurta, Parag Kumar Guha Natl Inst Technol Dept Comp Sci & Engn Durgapur 713209 India

An energy efficient content distribution technique in mobile ad hoc networks is proposed in this paper. based on bit rate, remaining energy and maximum allowable delay, a fitness value is evaluated for each node in the network. A set of nodes having maximum fitness values are elected as cluster heads. It is determined by applying teaching learning based optimization technique, only after the nodes satisfy an energy constraint. The clusters of nodes are formed by assigning the remaining nodes to their corresponding cluster heads. The cluster heads can receive the required packets from the base station via long range communication and forward the same to the cluster members. Efficient energy expenditure by this approach leads to an increased network lifetime. The experimental results show effectiveness of the proposed approach over K-means Clustering and U+M techniques in terms of reduced energy consumption and improved network lifetime. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Energy Mobile terminals Cluster heads Base station Bit rate teaching learning based optimization Lifetime

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Improved DV-Hop localization algorithm using teaching learning based optimization for wireless sensor networks

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TELECOMMUNICATION SYSTEMS 2018年第2期67卷 163-178页

作者： Sharma, Gaurav Kumar, Ashok Natl Inst Technol Dept Elect & Commun Engn Hamirpur 177005 Himachal Prades India

Node localization is one of the most critical issues for wireless sensor networks, as many applications depend on the precise location of the sensor nodes. To attain precise location of nodes, an improved distance vector hop (IDV-Hop) algorithm using teaching learning based optimization (TLBO) has been proposed in this paper. In the proposed algorithm, hop sizes of the anchor nodes are modified by adding correction factor. The concept of collinearity is introduced to reduce location errors caused by anchor nodes which are collinear. For better positioning coverage, up-gradation of target nodes to assistant anchor nodes has been used in such a way that those target nodes are upgraded to assistant anchor nodes which have been localized in the first round of localization. For further improvement in localization accuracy, location of target nodes has been formulated as optimization problem and an efficient parameter free optimization technique viz. TLBO has been used. Simulation results show that the proposed algorithm is overall 47, 30 and 22% more accurate than DV-Hop, DV-Hop based on genetic algorithm (GADV-Hop) and IDV-Hop using particle swarm optimization algorithms respectively and achieves high positioning coverage with fast convergence.

关键词： Correction factor DV-Hop algorithm Localization optimization teaching learning based optimization Wireless sensor networks

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Parameters identification of solar cell models using generalized oppositional teaching learning based optimization

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ENERGY 2016年 99卷 170-180页

作者： Chen, Xu Yu, Kunjie Du, Wenli Zhao, Wenxiang Liu, Guohai Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China E China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

This paper presents a new optimization method called GOTLBO (generalized oppositional teaching learning based optimization) to identify parameters of solar cell models. GOTLBO employs generalized opposition-based learning to basic teaching learning based optimization through the initialization step and generation jumping so that the convergence speed is enhanced. The performance of GOTLBO is comprehensively evaluated in thirteen benchmark functions and two parameter identification problems of solar cell models, i.e., single diode model and double diode model. Simulation results indicate the excellent performance of GOTLBO compared with four well-known evolutionary algorithms and other parameter extraction techniques proposed in the literature. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Solar cell models Parameter identification teaching learning based optimization Generalized opposition-based learning

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A new chaotic teaching learning based optimization for frequency reconfigurable antennas design

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2019年第2期36卷 1353-1360页

作者： Zadehparizi, Fatemeh Jam, Shahrokh Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran

In this paper, a new chaotic teaching learning based optimization (CTLBO) is proposed. TLBO is a rather newly proposed population-based algorithm. This algorithm has no control parameters for the tuning and has a simple structure. We improve its performance by chaotic maps. First, the presented CTLBO is tested on nine unimodal/multimodal benchmark functions. Then, chaotic sequences are applied as vectors with different initial values for design of a frequency reconfigurable antenna (FRA) as a practical example. Comparisons of the performance of this algorithm with those of the basic TLBO, genetic algorithm and particle swarm optimization show the ability of this algorithm in design of FRAs in terms of faster convergence and better performance. A prototype of the optimized antenna with CTLBO algorithm is fabricated and the simulation and measurement results agree suitably.

关键词： teaching learning based optimization Chaos theory Frequency reconfigurable antenna

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Optimal proton exchange membrane fuel cell modelling based on hybrid teaching learning based optimization - Differential Evolution algorithm

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AIN SHAMS ENGINEERING JOURNAL 2016年第1期7卷 347-360页

作者： Turgut, Oguz Emrah Coban, Mustafa Turhan Ege Univ Dept Mech Engn Fac Engn Izmir Turkey

Simulation proton exchange membrane fuel cell (PEMFC) performance accurately is a challenging process. Many mathematical models have been existed, yet due to lack of accurate parameter estimations, considerable errors might occur. Nowadays, meta-heuristic optimization algorithms have been successfully applied for parameter identification of PEMFC models. In this study, teaching learning based optimization method (TLBO) is hybridized with Differential Evolution (DE) algorithm for successful estimation of unknown PEMFC model parameters. Efficiency of the proposed algorithm is tested with several benchmark problems. A case study taken from the literature has been performed by hybrid TLBO-DE algorithm and other optimization methods such as Melody Search (MS), Backtracking Search (BS), Artificial Cooperative Search (ACS), Quantum behaved Particle Swarm optimization (QPSO), Bat algorithm (BAT), Intelligent Tuned Harmony Search (ITHS) and Cuckoo Search (CS). TLBO-DE algorithm surpasses all these optimizers in terms of solution quality and accuracy. (C) 2015 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： teaching learning based optimization Differential Evolution Parameter identification Polarization curve Proton exchange membrane fuel cell

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Building a novel classifier based on teaching learning based optimization and radial basis function neural networks for non-imputed database with irrelevant features

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APPLIED COMPUTING AND INFORMATICS 2022年第1/2期18卷 151-162页

作者： Dash, Ch. Sanjeev Kumar Behera, Ajit Kumar Dehuri, Satchidananda Cho, Sung-Bae Silicon Inst Technol Bhubaneswar India Fakir Mohan Univ Dept Informat & Commun Technol Balasore India Yonsei Univ Dept Comp Sci Soft Comp Lab Seoul South Korea

This work presents a novel approach by considering teaching learning based optimization (TLBO) and radial basis function neural networks (RBFNs) for building a classifier for the databases with missing values and irrelevant features. The least square estimator and relief algorithm have been used for imputing the database and evaluating the relevance of features, respectively. The preprocessed dataset is used for developing a classifier based on TLBO trained RBFNs for generating a concise and meaningful description for each class that can be used to classify subsequent instances with no known class label. The method is evaluated extensively through a few bench-mark datasets obtained from UCI repository. The experimental results confirm that our approach can be a promising tool towards constructing a classifier from the databases with missing values and irrelevant attributes.

关键词： Pattern recognition Imputation Classification Radial basis function neural networks teaching learning based optimization k-Nearest neighbor

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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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Multi-Objective parametric optimization during micro-EDM drilling of Ti-6Al-4 V using teaching learning based optimization algorithm

Multi-Objective parametric optimization during micro-EDM dri...

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International Conference on Advances in Materials and Mechanical Engineering. (ICAMME)

作者： Singh, Narender Bharti, Pushpendra S. GGS Indraprastha Univ Univ Sch Informat Commun & Technol Delhi 110078 India MSIT C-4 New Delhi 110058 India

The current paper investigates responses such as drilling rate (DR) and tool wear rate (TWR) experimentally for Electric discharge machining (EDM) during micro-hole drilling on titanium alloy with brass tool. The experiments are conducted using response surface methodology (RSM) based central composite design (CCD). Three process parameters like current, pulse-on-time and pulse-off-time are investigated for two responses: DR and TWR through experimentation. After conducting experiments, the effect of each parameter on responses has been found. Single and multi-objective optimization have been done using teaching learning based optimization (TLBO) algorithm to find the optimal combination of procedure parameters for maximum DR and minimum TWR. The confirmative test has been performed to verify the result of multi-objective optimization. A difference of<5% has been found between experimental and predicted values using the TLBO algorithm. The microstructure of the micro-drill machined surface is studied through SEM images. Copyright (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Electrical Discharge Machining teaching learning based optimization Tool wear rate Drilling rate Central composite design

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Multiple Flat Beams Generation Using Firefly and teaching learning based optimization Techniques 3rd

Multiple Flat Beams Generation Using Firefly and Teaching Le...

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3rd International Conference on Smart Computing and Informatics (SCI)

作者： Chaitanya, R. Krishna Rao, P. Mallikarjuna Raju, K. V. S. N. Raju, G. S. N. SRKR Engn Coll Dept ECE Bhimavaram India Andhra Univ Dept ECE Visakhapatnam Andhra Pradesh India Centurion Univ Dept ECE Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9789813296909;9789813296893

Multiple flat beams have an extensive range of applications in mobile and wireless communications. They facilitate many users to connect simultaneously. Multiple beam pattern can be produced utilizing both conventional and modern optimization techniques. teaching learning based optimization and Firefly are applied for Multibeam purpose in this paper. Multibeam with a beamwidth of 0.2 (in U = sin.) each are produced. In the present work, unsymmetric array antenna is utilized to generate the Multibeam pattern. The beams are well-formed, and they are observed to be optimized. The patterns are presented in U domain. The resultant distribution of antenna parameters is found, with the goal that they can be used directly in Array antenna design.

关键词： Firefly algorithm Linear array antenna teaching learning based optimization Multiple flat beams

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