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Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年 53卷 937-948页

作者： Roy, Provas Kumar Bhui, Sudipta Dr BC Roy Engn Coll Dept Elect Engn Durgapur 713206 W Bengal India

This paper proposes an efficient optimization approach, namely quasi-oppositional teaching learning based optimization (QOTLBO) for solving non-linear multi-objective economic emission dispatch (EED) problem of electric power generation with valve point loading. In this article, a non-dominated sorting QOTLBO is employed to approximate the set of Pareto solution through the evolutionary optimization process. The proposed approach is carried out to obtain EED solution for 6-unit, 10-unit and 40-unit systems. For showing the superiority of this optimization technique, numerical results of the four test systems are compared with several other EED based recent optimization methods. The simulation results show that the proposed algorithm gives comparatively better operational fuel cost and emission in less computational time compared to other optimization techniques. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Economic dispatch Emission Valve point loading Pareto front Opposition based learning teaching learning based optimization

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QoS Multicast Routing Using teaching learning based optimization

QoS Multicast Routing Using Teaching Learning Based Optimiza...

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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

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Cyber-Resilient Trading for Sustainable Energy Management: A Three-Phase Demand-Side Management Solution with Integrated Deep learning-based Renewable Energy Forecasting

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第2期60卷 2532-2541页

作者： Pati, Uttamarani Mistry, Khyati D. Sardar Vallabhbhai Natl Inst Technol Elect Engn Dept Surat 395007 India

Rising technological advancement has resulted in increased demand for power. As a result of this tendency, conventional fuel stocks have been severely exhausted. Energy suppliers are concerned about the sustainability of their grid's electricity dependability as fuel sources dwindle. Also, to reduce carbon footprints, governments throughout the world are exerting pressure on the energy sector to convert to cleaner energy sources. In order to lower the system's dependence on traditional fuel reserves during peak demand hours and progress towards introducing greener energy into the network with no discernible generation intermittency, an efficient scheduling model has been developed in this article to regulate end-user load as well as solar generation. The scheduling model is built with a long-short term memory (LSTM) network-based solar energy prediction approach integrated with a load and solar energy scheduling model based on teaching-learning optimization (TLBO). In addition to effective scheduling, a secure trade platform based on blockchain technology is being developed for utility and prosumer interaction in a cyber-secured environment. The suggested technique is intended for three-phased distribution networks, and its effectiveness is demonstrated using a 906-bus low voltage European distribution network. The proposed methodology is developed using MATLAB and Remix IDE interface. The effectiveness of the considered optimization in the proposed scheduling model is tested against another population-based optimization based identical scheduling model.

关键词： optimization Load modeling Job shop scheduling Renewable energy sources Mathematical models Costs Solar energy Blockchain based decentralized application load scheduling long-short term memory solar energy supply scheduling teaching learning based optimization

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Design of TMLAA for the Utilization of Radiated Harmonic Beam Over Digital Modulation Schemes

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MICROWAVE AND OPTICAL TECHNOLOGY LETTERS 2025年第1期67卷

作者： Varma, D. Suneel Ram, Gopi Kumar, G. Arun Astra Microwave Pvt Ltd Hyderabad India Natl Inst Technol Dept ECE Warangal India

This work presents the effective steering of multi-harmonic beams of a time-modulated linear antenna array (TMLAA) for multi-channel communication. A classroom learning-inspired teaching learning based optimization (TLBO) is utilized for active duration optimization of the 16-element TMLAA for harmonic steering. The simulation study employing the optimization technique effectively steered the first two harmonic beams with enhanced radiated power and considerable Sidelobe level (SLL) below - 20 dB $-20\,\mathrm{dB}$. Further, for practical validation, the measurement of the fabricated prototype is presented for one scenario. Finally, the use of harmonic beams for the communication links is validated by measuring the bit error rate (BER) of the considered digitally modulated scheme.

关键词： beam steering bit error rate harmonic beam steering phase shift keying teaching learning based optimization

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Group learning algorithm: a new metaheuristic algorithm

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NEURAL COMPUTING & APPLICATIONS 2023年第19期35卷 14013-14028页

作者： Rahman, Chnoor M. Charmo Univ Coll Sci Comp Dept Sulaymaniyah Iraq Amer Univ Iraq Sulaimani Informat Technol Dept Sulaymaniyah Iraq

Metaheuristics are intelligent optimization techniques that lead the searching procedure through utilizing exploitation and exploration. Increasing the number of hard problems with big data sets has encouraged researchers to implement novel metaheuristics and hybrid the existing ones to improve their performance. Hence, in this work, a novel metaheuristic called group learning algorithm is proposed. The main inspiration of the algorithm emerged from the way individuals inside a group affect each other, and the effect of group leader on group members. The two main steps of optimization, exploration and exploitation are outlined through integrating the behaviors of group members and the group leader to complete the assigned task. The proposed work is evaluated against a number of benchmarks. The produced results of classical benchmarks are compared against PSO, GWO, TLBO, BA, ALO, and SSA. In general, compared to other participated algorithms, out of 19 classical benchmarks, the proposed work showed better results in 11. However, the second best algorithm which is SSA performed better in 4 out of 19 benchmarks. To further evaluate the ability of the algorithm to optimize large scale optimization problems CEC-C06 2019 benchmarks are utilized. In comparison to other participated algorithms, the proposed work produced better results in most of the cases. Additionally, the statistical tests confirmed the significance of the produced results. The results are evidence that the proposed algorithm has the ability to optimize various types of problems including large scale optimization problems.

关键词： optimization Single objective optimization Benchmark Particle swarm optimization Grey wolf optimization algorithm teaching learning based optimization Population based algorithm Evolutionary algorithm

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Wrapper-based Feature Selection for Medical Diagnosis: The BTLBO-KNN Algorithm

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IEEE ACCESS 2023年 11卷 61368-61389页

作者： Seghir, Fateh Drif, Ahlem Selmani, Saadeddine Cherifi, Hocine Ferhat Abbas Univ Setif 1 Lab Syst Intelligents LSI Setif 19000 Algeria Ferhat Abbas Univ Setif 1 Fac Sci Comp Sci Dept Setif 19000 Algeria Univ Burgundy Lab Interdisciplinaire Carnot Bourgogne ICB UMR 6303 CNRS F-21078 Dijon France

Medical diagnosis research has recently focused on feature selection techniques due to the availability of multiple variables in medical datasets. Wrapper-based feature selection approaches have shown promise in providing faster and more cost-effective predictors. However, selecting the most relevant features from medical datasets to increase disease classification accuracy remains a challenging research issue. To address this challenge, we propose an effective wrapper-based feature selection approach called BTLBO-KNN. It combines an improved Binary teaching-learning based optimization (BTLBO) algorithm with the K-Nearest Neighbor (KNN) classifier to accelerate the convergence rate in finding the near-optimal features subset. BTLBO-KNN incorporates two new efficient binary teaching and learning processes, an abandoned learner's replacement mechanism, and a teacher knowledge improvement method. We extensively compare BTLBO-KNN with recent state-of-the-art wrapper-based feature selection approaches on COVID-19 and 23 gene-expression and medical datasets with different dimensional complexities. Our results demonstrate the superiority of BTLBO-KNN over its alternatives in terms of minimizing the number of selected features and the classification error rate.

关键词： Binary optimization algorithm classification feature selection K-nearest neighbor machine learning medical diagnosis teaching learning based optimization

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Novel multi objective evolutionary framework for solving next release problem

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第2期44卷 3315-3339页

作者： Ghasemi, Mohsen Bagherifard, Karamollah Parvin, Hamid Nejatian, Samad Islamic Azad Univ Dept Comp Engn Yasooj Branch Yasuj Iran Islamic Azad Univ Dept Comp Engn Nourabad Mamasani Branch Nourabad Mamasani Iran Islamic Azad Univ Dept Elect Engn Yasooj Branch Yasuj Iran

Software developers want to meet the requirements of customers in next versions. Choosing which set of requirements can be done according to cost and time is an NP-hard problem known as Next Release Problem (NRP). In this article, a multi objective evolutionary algorithm (MOEA) framework is proposed to solve NRP. The framework applies the nonrepetitive population, integrates solutions and external repository. Furthermore, a novel approach is implemented to satisfy the constraints of the problem. In this framework, six evolutionary algorithms are implemented and using seven quality indicators, the achieved results of that algorithms are compared with the original versions of same algorithms. Through using HV (the ratio of the region covered by Pareto Front) and NDS (the number of solutions in the Pareto Front) metrics, the effects of the proposed algorithms are compared with other works' results. The efficacy of the proposed MOEA framework is measured using three real world datasets. The gained results represent that the implemented algorithms perform better than other related algorithms previously published.

关键词： Next release problem multi-objective evolutionary algorithm search-based software engineering teaching learning based optimization non-repetitive population

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Coupled flexural torsional analysis and buckling optimization of variable stiffness thin-walled composite beams

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2022年第19期29卷 2795-2815页

作者： Mukherjee, Paulomi Punera, Devesh Mishra, Mayank Indian Inst Technol Bhubaneswar Sch Infrastruct Argul 752050 Khordha India Indian Inst Technol Madras Dept Appl Mech Madras Tamil Nadu India

Present work examines flexural-torsional coupling for static and stability analysis of thin-walled composite beams having variable stiffness. Variable stiffness composites (VSCs), manufactured with curvilinear fibers, are considered in this work. Compared to straight fiber laminates, variable angle tow (VAT) sections may significantly improve structural response as they redistribute the in-plane stresses. A generalized beam kinematic model which considers the effect of flexural, torsional and, warping deformation is adopted and using the principle of minimum potential energy, governing equations are derived. The variable fiber angles can be defined using a general Lagrangian polynomial and as a particular case, the solution is presented for linearly varying fiber angles. Stability analysis considers geometric nonlinearity. The present model neglects transverse shear deformations, and governing equations are numerically solved using a set of C (1) continuous finite elements. Important observations are made on the coupled response of thin-walled VSCs for static and stability analysis, which indicates the necessity of optimization study. An efficient 'teaching learning based optimization' (TLBO) algorithm is applied to obtain the most efficient curvilinear fiber path. The results show that lateral torsional buckling load capacity can be increased by approximately 15% in optimized VSCs compared to unidirectional fiber composites. The present kinematic model and numerical scheme can be easily extended to the optimization studies considering manufacturing constraints, which may be further implemented in commercial designs.

关键词： Flexural– torsional lateral buckling teaching learning based optimization thin-walled variable stiffness

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Design and Implementation of an Enhanced Demand-Side Management Solution For Distribution End Prosumers

Design and Implementation of an Enhanced Demand-Side Managem...

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IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies (GlobConHT)

作者： Pati, Uttamarani Mistry, Khyati D. Sardar Vallabhbhai Natl Inst Technol Elect Engn Dept Surat India

ISBN: (纸本)9798350332117

Power industry is presently undergoing tremendous revolutionary changes in order to move towards a decentralized power system network. The rising concerns about the contribution of greenhouse gases and carbon emissions to global warming have led utilities to opt for carbon-free green energy resources. This paper presents an effective load management option for the end users where the teaching-learning optimization-based demand-side management technique and integration of photovoltaic (PV) generation come into the frame. It also focuses on building a smooth, secure and flexible trading platform between the prosumers and the utility. The proposed methodology has been tested on a dataset containing load consumption and PV generation details of 50 prosumers. The methodology proposed has been designed using MATLAB 2020a and the Remix IDE interface.

关键词： teaching learning based optimization Demand-Side Management Photo-Voltaic Generation Blockchain-based Trading System

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Enhanced control of doubly fed induction generator based wind turbine system using soft computing assisted fractional order controller

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RENEWABLE ENERGY FOCUS 2022年 43卷 291-308页

作者： Karad, Shivaji Ganpat Thakur, Ritula Natl Inst Tech Teachers Training & Res Chandigarh 160019 India Natl Inst Tech Teachers Training & Res Sect 26 Chandigarh India

This paper proposes teaching learning based optimization (TLBO) algorithm assisted fractional order con-troller for control of Rotor side converter (RSC) and Grid side converter (GSC) for efficient and optimal control of doubly fed induction machine based wind turbine (WT) system. The proposed fractional order proportional integral (FOPI) controller is tuned using Genetic algorithm (GA), particle swarm optimiza-tion (PSO) and TLBO techniques. The rotating reference frame with stator flux alignment dependent field oriented vector control scheme is accomplished for decoupled optimal power control. Proposed DFIG based WT system implemented and simulated using MATLAB/Simulink. Simulation results of proposed TLBO based fractional order control are compared with GA and PSO tuned FOPI controller. The simulation results obtained are expedient that TLBO tuned FOPI is more prominent in DC link voltage control and shown promising results as compared to GA and PSO tuned FOPI Controller in terms of the of the solution time required and time domain performance parameters.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Fractional order control Genetic algorithm Particle swarm optimization Doubly fed induction generator teaching learning based optimization

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