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teaching-learning optimization based Adaptive Fuzzy Logic Controller for Frequency Control in an Autonomous Microgrid

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH 2017年第4期7卷 1842-1849页

作者： Anilkumar, A. Srikanth, N. V. Natl Inst Technol Warangal Dept Elect Engn Warangal Andhra Pradesh India

This paper addresses teaching learning optimization based adaptive fuzzy logic controller (AFLC) for frequency control in an autonomous AC microgrid. This autonomous microgrid consists of various renewable energy sources like PV and Wind power with high degrees of nonlinearities and also with variable load disturbances which can significantly, influences the system frequency. By considering all these uncertainties, the microgrid frequency control problem faces new challenges. Controlling of microgrid in autonomous mode is becoming a difficult task than in grid connected mode. In this paper novel AFLC is proposed for secondary frequency control. In proposed controller fuzzy input and output membership functions (MFs) scaling factors are tuned in online according to operating conditions using teaching learning based optimization technique (TLBO). In present work diesel engine generator is responsible for generation-load balance (secondary frequency control) in microgrid (MG). The robustness of the proposed controller is compared with conventional PI controller, fuzzy PI controller and PSO tuned fuzzy PI controller.

关键词： Microgrid adaptive fuzzy logic controller teaching-learning based optimization frequency control

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An improved TLBO based memetic algorithm for aerodynamic shape optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 57卷 1-15页

作者： Qu, Xinghua Zhang, Ran Liu, Bo Li, Huifeng Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Beihang Univ Astronaut Sch Beijing 100191 Peoples R China

Aerodynamic shape optimization (ASO) for aircraft is the focus of concern as well as the subject of substantial research issue in aerospace engineering. This paper proposes a novel TLBO (teaching-learning based optimization based) memetic algorithm (TLBO-MA) for optimizing the aerodynamic shape. In the proposed TLBO-MA, an adaptive teaching factor, conservation of information inspired operator and multi-meme learning are incorporated to enhance the searching behavior of standard TLBO. Simulation based on well-known benchmarks and ASO for HTV-2 prototype demonstrates the efficiency of the proposed TLBO-MA.

关键词： Aerodynamic shape optimization teaching-learning based optimization Memetic algorithm

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A soft computing-based study on WEDM optimization in processing Inconel 625

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NEURAL COMPUTING & APPLICATIONS 2021年第18期33卷 11985-12006页

作者： Sibalija, Tatjana V. Kumar, Sandeep Patel, G. C. Manjunath Jagadish Belgrade Metropolitan Univ Fac Informat Technol Fac Management Tadeusa Koscuska 63 Belgrade 11000 Serbia Bengal Coll Engn & Technol Dept Mech Engn Durgapur India Visvesvaraya Technol Univ Dept Mech Engn PES Inst Technol & Management Shivamogga Belagavi India Natl Inst Technol Dept Mech Engn Raipur Madhya Pradesh India

This work presents an examination of the wire electric discharge machining (WEDM) in processing Inconel 625. The major WEDM variables (pulse-on time, pulse-off time, servo voltage, wire feed rate) were experimentally investigated to address multiple aspects of this process, namely to reduce the gap current and surface roughness and increase the cutting speed. After testing the statistical significance of the major WEDM parameters for the three responses, an advanced statistical procedure was utilized for tackling correlations among outputs and their integration into the WEDM output performance in a fully objective manner. The Bayesian regularized neural network established a highly accurate process model that was engaged as the objective function for the evolutionary algorithms to identify the optimal machining conditions. The following algorithms were employed: particle swarm optimization, teaching-learning based optimization, grey wolf optimization and Jaya algorithm. Their results were thoroughly analyzed in terms of accuracy, i.e., repeatability of the resulting solutions, convergence speed and computational time, including assessment of the hyperparameter effects. The obtained optimal solution was highly convincing and it was successfully validated in a confirmation run. Therefore, the benefits of these findings are twofold, offering: (i) a thorough analysis of the four metaheuristics effectiveness in dealing with a real industrial problem, (ii) useful insights for controlling WEDM variables to enhance the technological, environmental and economic aspects at the same time.

关键词： Wire electric discharge machining Particle swarm optimization teaching-learning based optimization Grey wolf optimization Jaya algorithm Bayesian regularized artificial neural network

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An Effective TLBO-based Memetic Algorithm for Hypersonic Reentry Trajectory optimization

An Effective TLBO-Based Memetic Algorithm for Hypersonic Ree...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Qu, Xinghua Li, Huifeng Zhang, Ran Liu, Bo Beihang Univ Astronaut Sch Beijing Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China

ISBN: (纸本)9781509006229

In this paper, an effective teaching-learning based optimization (TLBO)-based memetic algorithm (TLBO-MA) is proposed to enhance the searching quality and efficiency of conventional TLBO, as its global fast coarse search capability and risks of getting prematurely stuck in local optima for the numerical optimization problems. In the proposed TLBO-MA, both TLBO-based operator and some special local searching operators are designed to balance the global exploration and local exploitation abilities. Some recent studies on the choice of local search method employed have shown that this choice significantly affects the efficiency of the memetic algorithm. To decide, at runtime, which local method is chosen, we adopt adaptive Meta-Lamarckian learning strategy. Finally, experimental studies with adaptive Meta-Lamarckian learning strategy on continuous benchmark problems and hypersonic trajectory optimization problem are presented. Simulation results on six benchmark problems and comparisons with Genetic Algorithm (GA), Particle Swarm optimization (PSO), and conventional TLBO indicate that the proposed TLBO-MA can not only effectively enhance the searching efficiency, but also greatly improve the searching quality. Simulation results on trajectory optimization demonstrate the feasibility of the proposed TLBO-MA to actual engineering problem.

关键词： teaching-learning based optimization local search method memetic algorithm benchmark problems

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A Simulation-based Approach to Multi-Item Multi-Echelon Spare Parts Inventory System optimization with Variable Warehouse Roles

A Simulation-Based Approach to Multi-Item Multi-Echelon Spar...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Coelho, Daniel B. P. Vianna, Wlamir O. L. Rodrigues, Leonardo R. Embraer SA Performance Software Dept Belo Horizonte MG Brazil Embraer SA Data Sci Dept Sao Jose Dos Campos Brazil Aeronaut Inst Technol Elect Engn Div Sao Jose Dos Campos Brazil

ISBN: (纸本)9781728169293

An optimized supply chain is essential for the success of large-scale industries. In order to meet the high availability level requirements, an efficient inventory system is crucial to reduce downtime when a failure event occurs. In a multi-echelon spare parts inventory system, each warehouse within the system may operate as a hub or a spoke. A hub is a warehouse that fulfills the demands of other warehouses, while a spoke is a warehouse that fulfills the demands of final customers. When the inventory system deals with multiple items, a fixed role (hub or spoke) is commonly assigned to each warehouse. However, this limitation may lead to sub-optimal solutions. If the optimization model allows each warehouse to have a different role for different items, a new degree of freedom is included and more efficient solutions can be found. In this paper, we propose a simulation-based optimization model to define the configuration of a multi-echelon spare parts inventory system of multiple items. The goal is to minimize total inventory costs, subject to a fill rate constraint. We relax the assumption that warehouses have a fixed role for all the items. Two algorithms are used to evaluate the model: the teaching-learning based optimization (TLBO) and the Simulated Annealing (SA) algorithms. A case study based on a spare parts inventory system of an aircraft manufacturer is used to compare the performance of the proposed model with the performance obtained considered fixed warehouse roles. The results showed that the proposed model provided a reduction of 6:8% in total cost, without violating the fill rate constraint.

关键词： optimization Multi-Echelon Inventory System Simulation-based optimization Simulated Annealing Meta-heuristics teaching-learning based optimization

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State of Charge Estimation of Lithium-ion Batteries in an Electric Vehicle using Hybrid Metaheuristic - Deep Neural Networks Models

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Energy Storage and Saving 2025年

作者： Zuriani Mustaffa Mohd Herwan Sulaiman Jeremiah Isuwa Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) 26600 Pekan Pahang Malaysia Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) 26600 Pekan Pahang Malaysia Department of Computer Science Federal University of Kashere Kashere 771103 Gombe Nigeria

Accurate estimation of the state of charge (SoC) of lithium-ion batteries (LIBs) in electric vehicles (EVs) is crucial for optimizing performance, ensuring safety, and extending battery life. However, traditional estimation methods often struggle with the nonlinear and dynamic behavior of battery systems, leading to inaccuracies that compromise the efficiency and reliability of electric vehicles. This study proposes a novel approach for SoC estimation in BMW electric vehicles by integrating a metaheuristic algorithm with deep neural networks. Specifically, teaching-learning based optimization (TLBO) is employed to optimize the weights and biases of the deep neural networks model, enhancing estimation accuracy. The proposed TLBO-deep neural networks (TLBO-DNNs) method was evaluated on a dataset of 1,064,000 samples, with performance assessed using mean absolute error (MAE), root mean square error (RMSE), and convergence value. The TLBO-DNNs model achieved an MAE of 3.4480, an RMSE of 4.6487, and a convergence value of 0.0328, outperforming other hybrid approaches. These include the barnacle mating optimizer–deep neural networks (BMO-DNNs) with an MAE of 5.3848, an RMSE of 7.0395, and a convergence value of 0.0492; the evolutionary mating algorithm-deep neural networks (EMA-DNNs) with an MAE of 7.6127, an RMSE of 11.2287, and a convergence value of 0.0536; and the particle swarm optimization–deep neural networks (PSO-DNNs) with an MAE of 4.3089, an RMSE of 5.9672, and a convergence value of 0.0345. Additionally, the TLBO-DNNs approach outperformed standalone models, including the autoregressive integrated moving average (ARIMA) model (MAE: 14.3301, RMSE: 7.0697) and support vector machines (SVMs) (MAE: 6.0065, RMSE: 8.0360). This hybrid TLBO-DNNs technique demonstrates significant potential for enhancing battery management systems (BMS) in electric vehicles, contributing to improved efficiency and reliability in electric vehicle operations.

关键词： Deep learning Deep neural networks Electric vehicle Machine learning optimization State of charge estimation teaching-learning based optimization

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Hybrid algorithms for handling the numerical noise in topology optimization

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Acta Mechanica Sinica 2020年第2期36卷 536-554页

作者： Pooya Rostami Javad Marzbanrad Vehicle Dynamical Systems Research Laboratory School of Automotive EngineeringIran University of Science and TechnologyNarmakTehranIran

This paper presents new hybrid methods for the identification of optimal topologies by combining the teaching-learning based optimization(TLBO)and the method of moving asymptotes(MMA).The topology optimization problem is parameterizing with a low dimensional explicit method called moving morphable components(MMC),to make the use of evolutionary algorithms more ***-based solvers have good performance in solving large-scale topology optimization ***,in unconventional cases same as crashworthiness design in which there is numerical noise in the gradient information,the uses of these algorithms are *** standard evolutionary algorithms can solve such problems since they don’t need gradient ***,they have a high computational *** paper is based upon the idea of combining metaheuristics with mathematical programming to handle the probable noises and have faster convergence *** to the ease of computations,the compliance minimization problem is considered as the case study and the artificial noise is added in gradient information.

关键词： Topology optimization teaching-learning based optimization Method of moving asymptotes Moving morphable components

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Hybrid teaching-learning with comprehensive learning capability for electromagnetic device design problems

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2023年第3期237卷 435-451页

作者： Niu, Qun You, Ming Hua, Dandan Yang, Zhile Shanghai Univ Sch Mech Engn & Automat Shanghai Key Lab Power Stn Automat Technol 99 Shangda Rd Shanghai 200444 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Guangdong Peoples R China

Many optimal design problems in the engineering field are nonlinear, multivariate, mixed integer, multimodal, and constrained. Meta-heuristic approaches have been widely used to solve these complex problems, but most of them are often sensitive to the settings of tuning parameters for different optimization problems, and suffer from premature convergence during the evolution process. This article proposes a novel hybrid teaching-learning-based optimization (HTLBO) algorithm to tackle this problem. A comprehensive teaching-learning mechanism with no adjustable parameters is introduced to improve the global optimal solution while in the meantime maintaining the solution diversity. The performance of the proposed HTLBO is tested on nine unconstrained benchmark functions and two nonlinear constrained benchmark functions with integer variables. Then the algorithm is applied to solve two significant electromagnetic design problems, that is, optimal brushless direct-current (BLDC) motor design and electromagnetic actuator geometric construction design. Simulation results on both the benchmark functions and practical engineering design problems confirm the efficiency and robustness of the proposed algorithm.

关键词： teaching-learning based optimization optimization design BLDC motor electromagnetic actuator

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Optimum design of cantilever retaining walls under seismic loads using a hybrid TLBO algorithm

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GEOMECHANICS AND ENGINEERING 2021年第3期24卷 237-251页

作者： Temur, Rasim Istanbul Univ Cerrahpasa Dept Civil Engn TR-34320 Istanbul Turkey

The main purpose of this study is to investigate the performance of the proposed hybrid teaching-learning based optimization algorithm on the optimum design of reinforced concrete (RC) cantilever retaining walls. For this purpose, three different design examples are optimized with 100 independent runs considering continuous and discrete variables. In order to determine the algorithm performance, the optimization results were compared with the outcomes of the nine powerful metaheuristic algorithms applied to this problem, previously: the big bang-big crunch (BB-BC), the biogeography based optimization (BBO), the flower pollination (FPA), the grey wolf optimization (GWO), the harmony search (HS), the particle swarm optimization (PSO), the teaching-learning based optimization (TLBO), the jaya (JA), and Rao-3 algorithms. Moreover, Rao-1 and Rao-2 algorithms are applied to this design problem for the first time. The objective function is defined as minimizing the total material and labor costs including concrete, steel, and formwork per unit length of the cantilever retaining walls subjected to the requirements of the American Concrete Institute (ACI 318-05). Furthermore, the effects of peak ground acceleration value on minimum total cost is investigated using various stem height, surcharge loads, and backfill slope angle. Finally, the most robust results were obtained by HTLBO with 50 populations. Consequently the optimization results show that, depending on the increase in PGA value, the optimum cost of RC cantilever retaining walls increases smoothly with the stem height but increases rapidly with the surcharge loads and backfill slope angle.

关键词： optimization retaining wall structural design seismic loads teaching-learning based optimization

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Optimum Reduction of Flexural Effect of Axially Symmetric Cylindrical Walls with Post-tensioning Forces

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KSCE JOURNAL OF CIVIL ENGINEERING 2018年第7期22卷 2425-2432页

作者： Bekdas, Gebrail Nigdeli, Sinan Melih Istanbul Univ Dept Civil Engn TR-34320 Istanbul Turkey

Post-tensioning of axially symmetric cylindrical walls has been used to prevent cracking and serviceability failures. In the case of axially symmetric cylindrical walls used for storing water, post-tensioning improves impermeability by reducing the tension loads generated by fluid loads. However;achieving these improvements in axially symmetric cylindrical walls requires determination of a suitable design for post-tensioning loads. By optimizing the locations and intensities of the post-tensioning loads, the effects on axially symmetric cylindrical walls can be minimized. In this study, four metaheuristic algorithms such as harmony search (HS), flower pollination algorithm, bat algorithm and teaching-learning based optimization are investigated for optimum design of posttension forces on axially symmetric cylindrical walls. The design involves the location and intensity of the post-tensioning cables. In the optimization process, analyses of cylindrical walls are conducted by a developed module using the superposition method (SPM). According to the analyses results, the optimization methods investigated are effective in reducing maximum longitudinal moments on cylindrical walls, but major difference in performance of the methods are seen.

关键词： axially symmetric cylindrical reinforced concrete walls optimization harmony search flower pollination algorithm bat algorithm teaching-learning based optimization

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