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<meta name="description" content="Search results for: TLBO"> <meta name="keywords" content="TLBO"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" 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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="TLBO"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 8</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: TLBO</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8</span> An Improved Discrete Version of Teaching–Learning-Based ‎Optimization for Supply Chain Network Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ehsan%20Yadegari">Ehsan Yadegari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> While there are several metaheuristics and exact approaches to solving the Supply Chain Network Design (SCND) problem, there still remains an unfilled gap in using the Teaching-Learning-Based Optimization (TLBO) algorithm. The algorithm has demonstrated desirable results with problems with complicated combinational optimization. The present study introduces a Discrete Self-Study TLBO (DSS-TLBO) with priority-based solution representation that can solve a supply chain network configuration model to lower the total expenses of establishing facilities and the flow of materials. The network features four layers, namely suppliers, plants, distribution centers (DCs), and customer zones. It is designed to meet the customer’s demand through transporting the material between layers of network and providing facilities in the best economic Potential locations. To have a higher quality of the solution and increase the speed of TLBO, a distinct operator was introduced that ensures self-adaptation (self-study) in the algorithm based on the four types of local search. In addition, while TLBO is used in continuous solution representation and priority-based solution representation is discrete, a few modifications were added to the algorithm to remove the solutions that are infeasible. As shown by the results of experiments, the superiority of DSS-TLBO compared to pure TLBO, genetic algorithm (GA) and firefly Algorithm (FA) was established. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20network%20design" title="supply chain network design">supply chain network design</a>, <a href="https://publications.waset.org/abstracts/search?q=teaching%E2%80%93learning-based%20optimization" title=" teaching–learning-based optimization"> teaching–learning-based optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=improved%20metaheuristics" title=" improved metaheuristics"> improved metaheuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20solution%20representation" title=" discrete solution representation"> discrete solution representation</a> </p> <a href="https://publications.waset.org/abstracts/184885/an-improved-discrete-version-of-teaching-learning-based-optimization-for-supply-chain-network-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184885.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">52</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7</span> Direct Torque Control of Induction Motor Employing Teaching Learning Based Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anam%20Gopi">Anam Gopi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The undesired torque and flux ripple may occur in conventional direct torque control (DTC) induction motor drive. DTC can improve the system performance at low speeds by continuously tuning the regulator by adjusting the Kp, Ki values. In this Teaching Learning Based Optimization (TLBO) is proposed to adjust the parameters (Kp, Ki) of the speed controller in order to minimize torque ripple, flux ripple, and stator current distortion. The TLBO based PI controller has resulted is maintaining a constant speed of the motor irrespective of the load torque fluctuations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=teaching%20learning%20based%20optimization" title="teaching learning based optimization">teaching learning based optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=direct%20torque%20control" title=" direct torque control"> direct torque control</a>, <a href="https://publications.waset.org/abstracts/search?q=PI%20controller" title=" PI controller"> PI controller</a> </p> <a href="https://publications.waset.org/abstracts/31465/direct-torque-control-of-induction-motor-employing-teaching-learning-based-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31465.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">585</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6</span> Structural Damage Detection Using Modal Data Employing Teaching Learning Based Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Subhajit%20Das">Subhajit Das</a>, <a href="https://publications.waset.org/abstracts/search?q=Nirjhar%20Dhang"> Nirjhar Dhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Structural damage detection is a challenging work in the field of structural health monitoring (SHM). The damage detection methods mainly focused on the determination of the location and severity of the damage. Model updating is a well known method to locate and quantify the damage. In this method, an error function is defined in terms of difference between the signal measured from ‘experiment’ and signal obtained from undamaged finite element model. This error function is minimised with a proper algorithm, and the finite element model is updated accordingly to match the measured response. Thus, the damage location and severity can be identified from the updated model. In this paper, an error function is defined in terms of modal data viz. frequencies and modal assurance criteria (MAC). MAC is derived from Eigen vectors. This error function is minimized by teaching-learning-based optimization (TLBO) algorithm, and the finite element model is updated accordingly to locate and quantify the damage. Damage is introduced in the model by reduction of stiffness of the structural member. The ‘experimental’ data is simulated by the finite element modelling. The error due to experimental measurement is introduced in the synthetic ‘experimental’ data by adding random noise, which follows Gaussian distribution. The efficiency and robustness of this method are explained through three examples e.g., one truss, one beam and one frame problem. The result shows that TLBO algorithm is efficient to detect the damage location as well as the severity of damage using modal data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=damage%20detection" title="damage detection">damage detection</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20model%20updating" title=" finite element model updating"> finite element model updating</a>, <a href="https://publications.waset.org/abstracts/search?q=modal%20assurance%20criteria" title=" modal assurance criteria"> modal assurance criteria</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20health%20monitoring" title=" structural health monitoring"> structural health monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=teaching%20learning%20based%20optimization" title=" teaching learning based optimization"> teaching learning based optimization</a> </p> <a href="https://publications.waset.org/abstracts/77962/structural-damage-detection-using-modal-data-employing-teaching-learning-based-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77962.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">215</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5</span> Algorithms Inspired from Human Behavior Applied to Optimization of a Complex Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Curteanu">S. Curteanu</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Leon"> F. Leon</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Gavrilescu"> M. Gavrilescu</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20A.%20Floria"> S. A. Floria</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optimization algorithms inspired from human behavior were applied in this approach, associated with neural networks models. The algorithms belong to human behaviors of learning and cooperation and human competitive behavior classes. For the first class, the main strategies include: random learning, individual learning, and social learning, and the selected algorithms are: simplified human learning optimization (SHLO), social learning optimization (SLO), and teaching-learning based optimization (TLBO). For the second class, the concept of learning is associated with competitiveness, and the selected algorithms are sports-inspired algorithms (with Football Game Algorithm, FGA and Volleyball Premier League, VPL) and Imperialist Competitive Algorithm (ICA). A real process, the synthesis of polyacrylamide-based multicomponent hydrogels, where some parameters are difficult to obtain experimentally, is considered as a case study. Reaction yield and swelling degree are predicted as a function of reaction conditions (acrylamide concentration, initiator concentration, crosslinking agent concentration, temperature, reaction time, and amount of inclusion polymer, which could be starch, poly(vinyl alcohol) or gelatin). The experimental results contain 175 data. Artificial neural networks are obtained in optimal form with biologically inspired algorithm; the optimization being perform at two level: structural and parametric. Feedforward neural networks with one or two hidden layers and no more than 25 neurons in intermediate layers were obtained with values of correlation coefficient in the validation phase over 0.90. The best results were obtained with TLBO algorithm, correlation coefficient being 0.94 for an MLP(6:9:20:2) – a feedforward neural network with two hidden layers and 9 and 20, respectively, intermediate neurons. Good results obtained prove the efficiency of the optimization algorithms. More than the good results, what is important in this approach is the simulation methodology, including neural networks and optimization biologically inspired algorithms, which provide satisfactory results. In addition, the methodology developed in this approach is general and has flexibility so that it can be easily adapted to other processes in association with different types of models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20behaviors%20of%20learning%20and%20cooperation" title=" human behaviors of learning and cooperation"> human behaviors of learning and cooperation</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20competitive%20behavior" title=" human competitive behavior"> human competitive behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20algorithms" title=" optimization algorithms"> optimization algorithms</a> </p> <a href="https://publications.waset.org/abstracts/149011/algorithms-inspired-from-human-behavior-applied-to-optimization-of-a-complex-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149011.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">107</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4</span> A Teaching Learning Based Optimization for Optimal Design of a Hybrid Energy System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Rouhani">Ahmad Rouhani</a>, <a href="https://publications.waset.org/abstracts/search?q=Masood%20Jabbari"> Masood Jabbari</a>, <a href="https://publications.waset.org/abstracts/search?q=Sima%20Honarmand"> Sima Honarmand</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces a method to optimal design of a hybrid Wind/Photovoltaic/Fuel cell generation system for a typical domestic load that is not located near the electricity grid. In this configuration the combination of a battery, an electrolyser, and a hydrogen storage tank are used as the energy storage system. The aim of this design is minimization of overall cost of generation scheme over 20 years of operation. The Matlab/Simulink is applied for choosing the appropriate structure and the optimization of system sizing. A teaching learning based optimization is used to optimize the cost function. An overall power management strategy is designed for the proposed system to manage power flows among the different energy sources and the storage unit in the system. The results have been analyzed in terms of technics and economics. The simulation results indicate that the proposed hybrid system would be a feasible solution for stand-alone applications at remote locations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20energy%20system" title="hybrid energy system">hybrid energy system</a>, <a href="https://publications.waset.org/abstracts/search?q=optimum%20sizing" title=" optimum sizing"> optimum sizing</a>, <a href="https://publications.waset.org/abstracts/search?q=power%20management" title=" power management"> power management</a>, <a href="https://publications.waset.org/abstracts/search?q=TLBO" title=" TLBO"> TLBO</a> </p> <a href="https://publications.waset.org/abstracts/35285/a-teaching-learning-based-optimization-for-optimal-design-of-a-hybrid-energy-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35285.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">578</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> A Review on Parametric Optimization of Casting Processes Using Optimization Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bhrugesh%20Radadiya">Bhrugesh Radadiya</a>, <a href="https://publications.waset.org/abstracts/search?q=Jaydeep%20Shah"> Jaydeep Shah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Indian foundry industry, there is a need of defect free casting with minimum production cost in short lead time. Casting defect is a very large issue in foundry shop which increases the rejection rate of casting and wastage of materials. The various parameters influences on casting process such as mold machine related parameters, green sand related parameters, cast metal related parameters, mold related parameters and shake out related parameters. The mold related parameters are most influences on casting defects in sand casting process. This paper review the casting produced by foundry with shrinkage and blow holes as a major defects was analyzed and identified that mold related parameters such as mold temperature, pouring temperature and runner size were not properly set in sand casting process. These parameters were optimized using different optimization techniques such as Taguchi method, Response surface methodology, Genetic algorithm and Teaching-learning based optimization algorithm. Finally, concluded that a Teaching-learning based optimization algorithm give better result than other optimization techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=casting%20defects" title="casting defects">casting defects</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=parametric%20optimization" title=" parametric optimization"> parametric optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=Taguchi%20method" title=" Taguchi method"> Taguchi method</a>, <a href="https://publications.waset.org/abstracts/search?q=TLBO%20algorithm" title=" TLBO algorithm"> TLBO algorithm</a> </p> <a href="https://publications.waset.org/abstracts/21826/a-review-on-parametric-optimization-of-casting-processes-using-optimization-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21826.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">728</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Parallel Gripper Modelling and Design Optimization Using Multi-Objective Grey Wolf Optimizer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Golak%20Bihari%20Mahanta">Golak Bihari Mahanta</a>, <a href="https://publications.waset.org/abstracts/search?q=Bibhuti%20Bhusan%20%20Biswal"> Bibhuti Bhusan Biswal</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20B.%20V.%20L.%20Deepak"> B. B. V. L. Deepak</a>, <a href="https://publications.waset.org/abstracts/search?q=Amruta%20Rout"> Amruta Rout</a>, <a href="https://publications.waset.org/abstracts/search?q=Gunji%20Balamurali"> Gunji Balamurali </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Robots are widely used in the manufacturing industry for rapid production with higher accuracy and precision. With the help of End-of-Arm Tools (EOATs), robots are interacting with the environment. Robotic grippers are such EOATs which help to grasp the object in an automation system for improving the efficiency. As the robotic gripper directly influence the quality of the product due to the contact between the gripper surface and the object to be grasped, it is necessary to design and optimize the gripper mechanism configuration. In this study, geometric and kinematic modeling of the parallel gripper is proposed. Grey wolf optimizer algorithm is introduced for solving the proposed multiobjective gripper optimization problem. Two objective functions developed from the geometric and kinematic modeling along with several nonlinear constraints of the proposed gripper mechanism is used to optimize the design variables of the systems. Finally, the proposed methodology compared with a previously proposed method such as Teaching Learning Based Optimization (TLBO) algorithm, NSGA II, MODE and it was seen that the proposed method is more efficient compared to the earlier proposed methodology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gripper%20optimization" title="gripper optimization">gripper optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristics" title=" metaheuristics"> metaheuristics</a>, <a href="https://publications.waset.org/abstracts/search?q=" title=""></a>, <a href="https://publications.waset.org/abstracts/search?q=teaching%20learning%20based%20algorithm" title=" teaching learning based algorithm"> teaching learning based algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20gripper%20design" title=" optimal gripper design"> optimal gripper design</a> </p> <a href="https://publications.waset.org/abstracts/86971/parallel-gripper-modelling-and-design-optimization-using-multi-objective-grey-wolf-optimizer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86971.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">188</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> Optimum Design of Steel Space Frames by Hybrid Teaching-Learning Based Optimization and Harmony Search Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alper%20Akin">Alper Akin</a>, <a href="https://publications.waset.org/abstracts/search?q=Ibrahim%20Aydogdu"> Ibrahim Aydogdu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study presents a hybrid metaheuristic algorithm to obtain optimum designs for steel space buildings. The optimum design problem of three-dimensional steel frames is mathematically formulated according to provisions of LRFD-AISC (Load and Resistance factor design of American Institute of Steel Construction). Design constraints such as the strength requirements of structural members, the displacement limitations, the inter-story drift and the other structural constraints are derived from LRFD-AISC specification. In this study, a hybrid algorithm by using teaching-learning based optimization (TLBO) and harmony search (HS) algorithms is employed to solve the stated optimum design problem. These algorithms are two of the recent additions to metaheuristic techniques of numerical optimization and have been an efficient tool for solving discrete programming problems. Using these two algorithms in collaboration creates a more powerful tool and mitigates each other’s weaknesses. To demonstrate the powerful performance of presented hybrid algorithm, the optimum design of a large scale steel building is presented and the results are compared to the previously obtained results available in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimum%20structural%20design" title="optimum structural design">optimum structural design</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20techniques" title=" hybrid techniques"> hybrid techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=teaching-learning%20based%20optimization" title=" teaching-learning based optimization"> teaching-learning based optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=harmony%20search%20algorithm" title=" harmony search algorithm"> harmony search algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20weight" title=" minimum weight"> minimum weight</a>, <a href="https://publications.waset.org/abstracts/search?q=steel%20space%20frame" title=" steel space frame"> steel space frame</a> </p> <a href="https://publications.waset.org/abstracts/25612/optimum-design-of-steel-space-frames-by-hybrid-teaching-learning-based-optimization-and-harmony-search-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25612.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">545</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a 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