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Performance enhancement of magnetic levitation system using teaching learning based optimization

ALEXANDRIA ENGINEERING JOURNAL 2018年第4期57卷 2427-2433页

作者： Yadav, Shekhar Verma, Santosh Kumar Nagar, Shyam Krishna MMM Univ Technol Dept Elect Engn Gorakhpur 273010 Uttar Pradesh India Banaras Hindu Univ Dept Elect Engn Indian Inst Technol Varanasi 221005 Uttar Pradesh India

This paper demonstrates the potency of evolution based optimization techniques in the sense of enhancing the system's performance. teaching learning based optimization (TLBO) is a well-known evolutionary algorithm used to optimize the parameters of the PID controller so as to improve the performance of the magnetic levitation system. The TLBO search algorithm is split into two phases, the teacher phase and the learner phase. The teacher phase is comprised of having minimum performance index as compared to learner phase. The learners improve their knowledge on the basis of teacher's performance. The parameters are tuned while minimizing the performance index of the system. The performance index incorporated in this paper is the integral time weighted square error (ITSE). The corroboration of the above technique is ended by comparing it with the conventional control techniques. (C) 2017 Faculty of Engineering, Alexandria 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： Magnetic levitation system PID controller teaching learning based optimization Performance index

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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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Multi-objective optimal power flow using quasi-oppositional teaching learning based optimization

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APPLIED SOFT COMPUTING 2014年 21卷 590-606页

作者： Mandal, Barun Roy, Provas Kumar Kalyani Govt Engn Coll Dept Elect Engn Kalyani W Bengal India Dr BC Roy Engn Coll Dept Elect Engn Durgapur W Bengal India

This paper describes teaching learning based optimization (TLBO) algorithm to solve multi-objective optimal power flow (MOOPF) problems while satisfying various operational constraints. To improve the convergence speed and quality of solution, quasi-oppositional based learning ( QOBL) is incorporated in original TLBO algorithm. The proposed quasi-oppositional teaching learning based optimization (QOTLBO) approach is implemented on IEEE 30-bus system, Indian utility 62-bus system and IEEE 118-bus system to solve four different single objectives, namely fuel cost minimization, system power loss minimization and voltage stability index minimization and emission minimization;three bi-objectives optimization namely minimization of fuel cost and transmission loss;minimization of fuel cost and L-index and minimization of fuel cost and emission and one tri-objective optimization namely fuel cost, minimization of transmission losses and improvement of voltage stability simultaneously. In this article, the results obtained using the QOTLBO algorithm, is comparable with those of TLBO and other algorithms reported in the literature. The numerical results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal non-dominated solutions of the multi-objective OPF problem. The simulation results also show that the proposed approach produces better quality of the individual as well as compromising solutions than other algorithms. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Optimal power flow teaching learning based optimization Quasi oppositional based learning Multi objective optimization Voltage stability index

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Optimal short-term hydro-thermal scheduling using quasi-oppositional teaching learning based optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2013年第10期26卷 2516-2524页

作者： Roy, Provas Kumar Sur, Aditi Pradhan, Dinesh Kumar Dr BC Roy Engn Coll Dept Elect Engn Durgapur W Bengal India Dr BC Roy Engn Coll Dept Informat Technol Durgapur W Bengal India

This paper presents a new approach for solving short-term hydrothermal scheduling (HTS) using an integrated algorithm based on teaching learning based optimization (TLBO) and oppositional based learning (OBL). The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydro reservoirs, water transport delay and scheduling time linkage that make the problem of optimization difficult using standard optimization methods. To overcome these problems, the proposed quasi-oppositional teaching learning based optimization (QOTLBO) is employed. To show its efficiency and robustness, the proposed QOTLBO algorithm is applied on two test systems. Numerical results of QOTLBO are compared with those obtained by two phase neural network, augmented Lagrange method, particle swarm optimization (PSO), improved self-adaptive PSO (ISAPSO), improved PSO (IPSO), differential evolution (DE), modified DE (MDE), fuzzy based evolutionary programming (Fuzzy EP), clonal selection algorithm (CSA) and TLBO approaches. The simulation results reveal that the proposed algorithm appears to be the best in terms of convergence speed, solution time and minimum cost when compared with other established methods. This method is considered to be a promising alternative approach for solving the short-term HTS problems in practical power system. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Opposition based learning Hydrothermal scheduling teaching learning based optimization Quasi-oppositional point

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Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年 63卷 534-545页

作者： Sultana, Sneha Roy, Provas Kumar Dr BC Roy Engn Coll Dept Elect Engn Durgapur W Bengal India

This paper presents a novel quasi-oppositional teaching learning based optimization (QOTLBO) methodology in order to find the optimal location of distributed generator to simultaneously optimize power loss, voltage stability index and voltage deviation of radial distribution network. The basic disadvantage of the original teaching learning based optimization (TLBO) algorithm is that it gives a near optimal solution rather than an optimal one in a limited iteration cycles. In this paper, opposition based learning (OBL) and quasi OBL concepts are introduced in original TLBO algorithm for improving the convergence speed and simulation results of TLBO. In order to show the effectiveness and superiority, the proposed algorithms are tested on 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results of the proposed methods are compared with those obtained by other artificial intelligence techniques like GA/PSO, GA, PSO and loss sensitivity factor simulated annealing (LSFSA). The results show that the QOTLBO surpasses the other techniques in terms of solution quality. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Radial distribution system Distributed generators Loss reduction Evolutionary algorithms teaching learning based optimization Quasi-opposition teaching learning based optimization

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Optimal capacitor placement in radial distribution systems using teaching learning based optimization

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2014年第Jan.期54卷 387-398页

作者： Sultana, Sneha Roy, Provas Kumar Dr BC Roy Engn Coll Dept Elect Engn Durgapur W Bengal India

This paper presents teaching learning based optimization (TLBO) approach to minimize power loss and energy cost by optimal placement of capacitors in radial distribution systems. The proposed algorithm is based on two basic concept of education namely teaching phase and learning phase. In first phase, learners improve their knowledge or ability through the teaching methodology of teacher and in second part learners increase their knowledge by interactions among themselves. To check the feasibility, the proposed method is applied on standard 22, 69, 85 and 141 bus radial distribution systems. Numerical experiments are included to demonstrate that the proposed TLBO can obtain better quality solution than many existing techniques like genetic algorithm (GA), particle swarm optimization (PSO), direct search algorithm (DSA) and mixed integer linear programming (MILP) approach. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Radial distribution system Optimal capacitor placement Optimal sizing Loss reduction teaching learning based optimization

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Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期53卷 123-134页

作者： Mandal, Barun Roy, Provas Kumar Kalyani Govt Engn Coll Dept Elect Engn Kalyani W Bengal India Dr BC Roy Engn Coll Dept Elect Engn Durgapur W Bengal India

This paper presents a newly developed teaching learning based optimization (TLBO) algorithm to solve multi-objective optimal reactive power dispatch (ORPD) problem by minimizing real power loss, voltage deviation and voltage stability index. To accelerate the convergence speed and to improve solution quality quasi-opposition based learning (QOBL) concept is incorporated in original TLBO algorithm. The proposed TLBO and quasi-oppositional TLBO (QOTLBO) approaches are implemented on standard IEEE 30-bus and IEEE 118-bus test systems. Results demonstrate superiority in terms of solution quality of the proposed QOTLBO approach over original TLBO and other optimization techniques and confirm its potential to solve the ORPD problem. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Optimal reactive power dispatch teaching learning based optimization Opposition based learning Evolutionary algorithm Voltage stability index Voltage profile

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A constrained non-linear optimization model for fuzzy pairwise comparison matrices using teaching learning based optimization

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APPLIED INTELLIGENCE 2016年第3期45卷 652-661页

作者： Goyal, Raman Kumar Kaushal, Sakshi Panjab Univ Univ Inst Engn & Technol Comp Sci & Engn Chandigarh 160014 India

Non-linear optimization models have been recently proposed to derive crisp weights from fuzzy pairwise comparison matrices. In this paper, a TLBO (teaching learning based optimization) based solution is presented for solving an optimization model as a system of non-linear equations to derive crisp weights from fuzzy pairwise comparison matrices in AHP (Analytic Hierarchy Process). This fuzzy-AHP method is named as TLBO-1. It has been found that TLBO-1 can lead to inconsistent or less consistent weights. To solve the problem of inconsistent weights, a new constrained non-linear optimization model is proposed in this paper. This model is based on the min-max approach for fuzzy pairwise comparison ratios of weights. TLBO is again used to solve this optimization model, and crisp weights are derived. This fuzzy AHP method is named as TLBO-2. The effectiveness of the proposed model is illustrated by three examples. For each example, the consistency of the derived crisp weights is compared with other optimization models. The results show that the TLBO-2 method can derive more consistent weights for the fuzzy AHP based Multi-Criteria Decision Making (MCDM) systems as compared to the other optimization models.

关键词： Multi-criteria decision making Fuzzy analytic hierarchy process teaching learning based optimization Comparison matrices Priority weights

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Source and transmission line maintenance outage scheduling in a power system using teaching learning based optimization algorithm

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APPLIED SOFT COMPUTING 2014年 21卷 72-83页

作者： Abirami, M. Ganesan, S. Subramanian, S. Anandhakumar, R. Annamalai Univ Fac Engn & Technol Dept Elect Engn Annamalainagar 608002 Tamil Nadu India

In order to enhance the performance and lifetime of any equipment, maintenance is essential. The major power system components including generators and transmission lines require periodical maintenance and in this regard, the present work details Integrated Maintenance Scheduling (IMS) for the secure operation. The IMS problem has been formulated as a complex optimization problem that affects unit commitment and economic dispatch schedules. Most of the methodologies adopt decomposition approaches for the solution of IMS. In this work, teaching learning based optimization (TLBO) has been used as a prime optimization tool as it has been proved to be an effective optimization algorithm when applied to various practical optimization problems and its implementation is simple involving less computational effort. The methodology has been tested on standard test systems and it works well while including generator contingency. Numerical results comparison indicates that this method is a promising alternative for solving IMS problem. (C) 2014 Elsevier B.V. All rights reserved.

关键词： teaching learning based optimization Integrated maintenance scheduling Generator outage Security constrained unit commitment Planning horizon

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

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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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