Teaching Learning Based Optimization Algorithm Research Articles

MapReduce Teaching Learning Based Optimization Algorithm for Solving CEC-2013 LSGO Benchmark Testsuit

MapReduce Teaching Learning Based Optimization Algorithm for Solving CEC-2013 LSGO Benchmark Testsuit

Optimal planning of electric vehicle charging stations and distributed generators with network reconfiguration in smart distribution networks considering uncertainties

This paper proposes an optimal planning technique for placing the multiple renewable energy (RE) based distributed generators (DGs), Distribution Static Compensators (DSTATCOMs), and electric vehicle charging stations (EVCSs) in the radial distribution network (RDN) considering the related uncertainties. This approach gives optimal placement and sizes for DGs and DSTATCOMs as well as a number of electric vehicles (EVs) that can be charged at the EVCSs by considering the network reconfiguration (NR). The optimal allocation of EVCSs fulfills the power demand from EVs at various locations and minimizes the negative impact on the power network. The RE-based DGs considered for this work are solar photovoltaic (PV) and wind. The uncertainties related to RE-based DGs and EVCSs have been modeled by using the probabilistic-based two-point estimate method (2PEM). The best locations and sizes are identified by optimizing the individual objectives that is active power losses and voltage stability index (VSI) using the teaching learning based optimization (TLBO) algorithm. Then both objectives are optimized by using the non-dominated sorting-based TLBO algorithm. Furthermore, the optimal planning approach is implemented on IEEE 33 and 69 bus test systems to demonstrate the suitability, practicality, and efficiency of the proposed optimal planning strategy. The obtained results reveal that the proposed technique is beneficial for determining the optimal locations for DGs, DSTATCOMs, and EVCSs without affecting the grid stability. The proposed planning approach can search better network structure with reduced power losses and voltage deviation, enhanced voltage profile, and improved voltage stability.

Modelling of Steady-State Seepage of an Embankment Dam Using Teaching-Learning Based Optimization Algorithm

The goal of the this study is to investigate the applicability of the teaching-learning based optimization (TLBO) algorithm for modeling seepage in embankment dams. The input parameters selected for the models to be built are the values of permeability (ks), van Genuchten's suitability parameters α and n, whose effect on seepage has been investigated over the years due to their uncertainties. The validity of the TLBO was compared with that of conventional regression analysis (CRA) methods. Both methods were utilized with different regression forms. The parameters chosen as input are modeled as random variables with a log-normal distribution, and total discharge (Q) was obtained. Four statistical indices, that is, root mean square error, mean absolute error, average relative error and coefficient of determination, were used to evaluate the performance of the models. The equations obtained using TLBO algorithms can predict the total discharge in embankment dams better than CRA. In addition, the reliability of TLBO has been demonstrated by conducting analyses using the outputs of CRA as a benchmark.

Optimization of Cost-Based Hybrid Flowshop Scheduling Using Teaching-Learning-Based Optimization Algorithm

A cost-based hybrid flowshop scheduling (CHFS) combines flow shop and job shop elements, with cost considerations as a key indicator. CHFS is a complex combinatorial optimization challenge encountered in real-world manufacturing and production environments. This paper investigates the optimization of a CHFS problem using the Teaching Learning-Based Optimization (TLBO) algorithm. Effective CHFS is crucial for achieving production balance, reducing costs, and improving customer satisfaction. The authors formulate the CHFS scheduling problem and propose applying the TLBO algorithm to minimize total costs, including labor, energy, maintenance, and delay expenses. The performance of the TLBO technique is evaluated through computational experiments on various CHFS problem instances. The results demonstrate the effectiveness of the TLBO algorithm, which achieved the best results in 42% of the test cases, surpassing other algorithms like the Grey Wolf Optimizer and Particle Swarm Optimization. Additionally, the TLBO algorithm had the highest average performance ranking across the comparative algorithms. The study highlights the potential of the TLBO algorithm as an efficient optimization tool for complex manufacturing scheduling problems.

Application of water cycle algorithm with demand follows green level and nonlinear power pattern of the product for an inventory system

It is commonly known that a number of variables, including price, supply levels, time, and green level, affect how quickly certain things are in demand. Furthermore, the inventory carrying cost is considered to be a nonlinear representation of time and is subject to variation throughout time. More precisely, it rises with time since longer storage times necessitate more costly warehouse space. This study presents a fully backlogged situation inventory system for a single commodity where the product’s selling price, green level, and time are used to simultaneously compute the demand rate in accordance with a power pattern. Purchase price is determined by the product’s nonlinear green level. Complete backorders are available for shortages. The impact of the product’s selling price, green level and time power function are combined to determine the product’s demand. Moreover, the holding cost also rises as the product is stored for a longer period of time. The primary goal is to determine the best inventory policy to maximise total profit per unit of time. Though the problem is highly nonlinear in nature. Hence, we cannot solve it analytically. To overcome these difficulties, we have applied several well-known popular metaheuristic algorithms (Water Cycle Algorithm (WCA), Artificial Electric Field Algorithm (AEFA), Teaching Learning Based Optimization Algorithm (TLBOA), Grey Wolf Optimizer Algorithm (GWOA), Sparrow Search Algorithm (SSA), Whale Optimizer Algorithm (WOA), Prairie Dog Optimization Algorithm (PDOA), Gazelle Optimization Algorithm (GOA), A Sinh Cosh Optimizer Algorithm (SCHOA) and White Sherk Optimizer Algorithm (WSOA), Archimedes Optimization Paradigm Algorithm (AOPA), Marine Predator Optimization Algorithm (MPOA), Geyser Inspired Algorithm (GIA), Runge Kutta Optimization Algorithm (RKOA), Lungs Performance-based Optimization Algorithm (LPOA) and Dwarf Mongoose Optimization Algorithm (DMOA)). It is observed that WCA perform better than other algorithms with respect to the convergence rate. A numerical example is taken in order to validate the proposed model. Finally, a post optimality analysis is performed in order to make a fruitful conclusion.

Analysis of reduction of carbon emission and dynamic service policies in a green manufacturing system under isoperimetric fixed servicing budget constraint

In the recent highly saturated, fluctuated as well as competitive marketing situation, economic growth, stability and survival of a manufacturing company are substantial factors for the competent authority of a manufacturing firm. Again, due to the harmful effects of different chemicals and preservative oriented products, consumers gradually show their sensible interest towards the green products and their benefits. Furthermore, emission reduction and carbon emission tax become major issues on the sustainable growth and reputation of a manufacturing company. Fundamentally, considering these factors, this study demonstrates the prior decisions regarding best-found policy of a manufacturing company considering the effects of service with sold eco-friendly (green) products under the effects of emission taxation and reduction policies. The demand effected by level of greenness, price and dynamic service provided to the consumers. Now, due to the budget constraint, manufacturer estimates a fixed budget due to service to the consumers along with the items sold. Again, based on the carbon emission reduction policy, two distinct cases are arisen which are analyzed separately. Due to the budget constraint, maximization problem associated to the model becomes an isoperimetric control problem. Then to illustrate the realistic feasibility the proposed model, a physical example is taken under consideration and it is solved using Teaching–learning-based optimization algorithm (TLBOA) considering each of the cases. Finally, the sensitivity analyses are performed to get some important implications for the manager of the proposed manufacturing system.

A four-stage strategy for solving AC transmission expansion planning problem in large power system based on differential evolution algorithm and teaching–learning-based optimization algorithm

A four-stage strategy for solving AC transmission expansion planning problem in large power system based on differential evolution algorithm and teaching–learning-based optimization algorithm

Multi-objective optimization of a dual fuel CI engine powered with syngas and pilot diesel using TLBO algorithm: A metaheuristic approach

Abstract The thermochemical conversion of biomass into producer gas presents an attractive alternative fuel option for compression ignition (CI) engines, making biomass gasification a critical driver for achieving sustainable development goals. Considering the application of producer gas (PG) in CI engine, the most potential gases include H2 and CO as main fuel compounds and it is crucial to comprehensively understand the impact of these two gas components on the engine behaviour. Nowadays, artificial intelligence-powered models are frequently applied for simulating engines that run on a single type of fuel. However, their usage is not as common when it comes to modeling dual-fuel CI engines run on synthetic producer gas or syngas. The present study explores the feasibility of optimizing operational parameters, such as engine load and syngas composition, in improving the efficiency and lowering the levels of pollutants emitted by a 3.5 kW CI engine operated under dual fuel (DF) mode using syngas as primary fuel and diesel as pilot fuel. The performance and emission characteristics of syngas (H2:CO) is examined by studying its behaviour in four different combinations. The compositions of syngas are prepared based on the volumetric percentage of the H2 and CO and is inducted into the combustion chamber using a novel venturi-type air-gas mixer. In the present study, an intelligent metaheuristics-based optimization algorithm i.e., Teaching–Learning Based Optimization (TLBO) is developed and introduced, to develop a predictive model within constrained range of engine operating conditions. Further, the algorithm is used to estimate multiple engine performance characteristics simultaneously viz., brake thermal efficiency (BTE), unburned hydrocarbons (HC), and carbon monoxide (CO). The resultant findings identify the optimal engine load of 68.87% and the ideal syngas composition of 63.9% H2 and 49.5% CO as key parameters for maximizing engine efficiency while minimizing exhaust emission. At these optimized operating condition, 19.49% BTE is observed, while HC and CO emission was found to be 384.6 ppm and 445.33 ppm respectively. This shows the effective and efficiency of the proposed algorithm.

A test of meta-heuristic algorithms for parameter extraction of next-generation solar cells with S-shaped current–voltage curves

Identifying parameters of photovoltaic (PV) models based on measured current–voltage (IV) characteristic curves is critical for simulating, evaluating, and controlling PV systems. IV characteristics of the latest-generation solar cells (SCs) often display an S-shaped deformation. In this paper, we explore the potential of meta-heuristic algorithms to address the parameter estimation problems associated with PV cells that exhibit S-shaped IV characteristics. This estimation is performed within the framework of the opposed two-diode model. We implemented a total of 14 algorithms from various classes to extract the SC parameters from synthetic IV curves, which were generated using a range of parameter values. The results were compared by using nonparametric statistical methods. These methods include the Wilcoxon signed-rank test for pairwise comparisons, and the Friedman, Friedman Aligned, and Quade tests for multiple comparisons. Comprehensive results and analyses show that the STLBO (Simplified teaching–learning based optimization algorithm) and ADELI (Adaptive differential evolution with the Lagrange interpolation argument) algorithms demonstrate highly competitive performance in terms of accuracy and reliability. This paper underscores the efficacy of advanced meta-heuristic algorithms in solving complex non-linear optimization problems in the domain of photovoltaic research, particularly concerning the unique challenges posed by S-shaped IV characteristics of new-generation solar cells.

Optimization Of Cantilever Retaining Wall Design Using Improved Teaching-Learning-Based Optimization Algorithms

Retaining structures play a crucial role in geotechnical engineering to support soil levels, prevent slope failure, and create flat surfaces for construction. Designing these structures involves optimizing internal and external stability while minimizing material usage and cost. This study focused on optimizing reinforced concrete cantilever retaining walls using the Teaching-Learning Based Optimization (TLBO) algorithm and an improved version (I-TLBO) with agents. In the context of the study, geometric-structural design variables, geotechnical -structural constraints, and optimization processes were examined. Minimizing weight and minimizing cost of the wall were the objectives considered in the cantilever retaining wall design process. The optimization results were compared with other algorithms in the literature, such as genetic algorithms, evolutionary strategies, and particle swarm optimization. The improved TLBO algorithm demonstrated superior performance, achieved lower design dimensions, and reduced costs. It provided more efficient solutions that pushed design constraints closer to their limits, resulting in a cost-effective and structurally sound cantilever retaining wall design. As a result of the study, the I-TLBO algorithm was found to be more cost and weight-effective than other methods in the optimization of cantilever retaining wall design.

Enhancing Electrical Distribution System Efficiency: A Software Tool Design for Conductor Cross-Section Optimization With TLBO Algorithm

In radial distribution networks with many separate branches and sections, it is very difficult to calculate the optimum conductor cross-section. This work introduces a new tool for optimizing conductor cross-sectional area in electrical distribution systems by utilizing the Teaching-Learning-Based Optimization (TLBO) algorithm. The tool can optimize the conductor crossection of the multisection, branching distribution systems. The maximum current carrying capacity constraint is taken into consideration when formulating the objective function to choose the ideal conductor size for each network segment. The optimal conductor sizes are determined by both desired percent voltage drop and current carrying capacity of the conductor. By calculating the currents drawn from the line segments in advance, the search space of the optimization algorithm is narrowed. MATLAB and Excel were used to determine the ideal conductor size. The conductor, which is chosen using the suggested method, will preserve appropriate voltage levels in radial distribution systems while optimizing the overall savings in conducting material and energy loss costs. The outcomes show that the optimal selection of conductor problem can be solved by the TLBO algorithm in a practical and effective manner. Results of testing the suggested tool on a radial distribution system are noteworthy.

Teaching–Learning-Based Optimization Algorithm with Stochastic Crossover Self-Learning and Blended Learning Model and Its Application

This paper presents a novel variant of the teaching–learning-based optimization algorithm, termed BLTLBO, which draws inspiration from the blended learning model, specifically designed to tackle high-dimensional multimodal complex optimization problems. Firstly, the perturbation conditions in the “teaching” and “learning” stages of the original TLBO algorithm are interpreted geometrically, based on which the search capability of the TLBO is enhanced by adjusting the range of values of random numbers. Second, a strategic restructuring has been ingeniously implemented, dividing the algorithm into three distinct phases: pre-course self-study, classroom blended learning, and post-course consolidation; this structural reorganization and the random crossover strategy in the self-learning phase effectively enhance the global optimization capability of TLBO. To evaluate its performance, the BLTLBO algorithm was tested alongside seven distinguished variants of the TLBO algorithm on thirteen multimodal functions from the CEC2014 suite. Furthermore, two excellent high-dimensional optimization algorithms were added to the comparison algorithm and tested in high-dimensional mode on five scalable multimodal functions from the CEC2008 suite. The empirical results illustrate the BLTLBO algorithm’s superior efficacy in handling high-dimensional multimodal challenges. Finally, a high-dimensional portfolio optimization problem was successfully addressed using the BLTLBO algorithm, thereby validating the practicality and effectiveness of the proposed method.

Investigation of rank order centroid method for optimal generation control.

Multi-criteria decision-making (MCDM) presents a significant challenge in decision-making processes, aiming to ascertain optimal choice by considering multiple criteria. This paper proposes rank order centroid (ROC) method, MCDM technique, to determine weights for sub-objective functions, specifically, addressing issue of automatic generation control (AGC) within two area interconnected power system (TAIPS). The sub-objective functions include integral time absolute errors (ITAE) for frequency deviations and control errors in both areas, along with ITAE of fluctuation in tie-line power. These are integrated into an overall objective function, with ROC method systematically assigning weights to each sub-objective. Subsequently, a PID controller is designed based on this objective function. To further optimize objective function, Jaya optimization algorithm (JOA) is implemented, alongside other optimization algorithms such as teacher-learner based optimization algorithm (TLBOA), Luus-Jaakola algorithm (LJA), Nelder-Mead simplex algorithm (NMSA), elephant herding optimization algorithm (EHOA), and differential evolution algorithm (DEA). Six distinct case analyses are conducted to evaluate controller's performance under various load conditions, plotting data to illustrate responses to frequency and tie-line exchange fluctuations. Additionally, statistical analysis is performed to provide further insights into efficacy of JOA-based PID controller. Furthermore, to prove the efficacy of JOA-based proposed controller through non-parametric test, Friedman rank test is utilized.

Application of the teaching–learning-based optimization algorithm to an analytical model of thunderstorm outflows to analyze the variability of the downburst kinematic and geometric parameters

Application of the teaching–learning-based optimization algorithm to an analytical model of thunderstorm outflows to analyze the variability of the downburst kinematic and geometric parameters

Intelligent optimization algorithm-driven informational teaching model for English reading and writing in universities

English reading and writing are important parts of language teaching. In order to improve the English reading and writing ability of college students, the TLBO (teaching learning-based optimization) algorithm is used in this research to improve the way that English reading and writing are taught in colleges and universities. It is chosen as the primary model for this study. The TLBO algorithm is further optimized in this paper, and a convergence analysis is performed between the optimized model M-TLBO (multi-learning teaching learning-based optimization) algorithm and other TLBO algorithms in order to address the issues that the TLBO algorithm has an excessively single teaching ability and readily settles into local optimal solutions for some large-scale complex problems. In terms of stability and convergence accuracy, M-TLBO outperforms other algorithms. In order to investigate the impact of the M-TLBO algorithm on students’ writing performance, this paper uses the teaching-learning optimization algorithm to conduct a pre-and post-test on students’ English reading and writing performance in five dimensions. The study’s findings revealed that students’ pre-test writing scores had a mean value of 8.4770 and a standard deviation of 1.72449, and that their post-study writing scores had increased by 5.05 points. The English reading and writing information-based teaching model can improve students’ English writing performance. It is hoped to promote the development of English teaching and improve the efficiency of students’ English learning.

A novel solution for the power transmission congestion of deregulated power system using TCSC and TLBO algorithm

The expansion of the electrical power sector has introduced heightened competition in electric markets, compelling utilities to enhance the efficiency of their power system equipment to provide customers with cost-effective and premium-quality power. Deregulation has, however, led to challenges such as congestion in transmission lines, adversely affecting power quality, voltage profiles, and system security. The congestion can be alleviated either by technical or non-technical methods. To address this issue, the manuscript proposes the implementation of Thyristor Control Series Capacitors (TCSC) as a technical solution for congestion mitigation. TCSC is used as a variable reactance device. The study employs line utilization factors (LUF) and disparity line utilization factors (DLUF) to obtain the optimal locations for TCSC installation. Additionally, the Teaching-Learning-Based Optimization (TLBO) algorithm is employed for the optimization of TCSC reactance. The manuscript also suggests another approach by simultaneously optimizing the location and parameters of multiple TCSCs using the TLBO algorithm without using the sensitivity factors. Comparative analyses with other optimization algorithms like gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) are conducted to determine the superior method for ensuring stable system operation. By application on the standard IEEE-30 bus system, the TLBO algorithm is validated, demonstrating its effectiveness in mitigating congestion by minimizing power loss, and voltage deviation, and enhancing security margins. The findings contribute valuable insights for practitioners seeking robust solutions in managing congestion within deregulated electric markets.

Integrated neural network and evolutionary algorithm approach for liver fibrosis staging: Can artificial intelligence reduce patient costs?

Staging liver fibrosis is important, and liver biopsy is the gold standard diagnostic tool. We aim to design and evaluate an artificial neural network (ANN) method by taking advantage of the Teaching Learning-Based Optimization (TLBO) algorithm for the prediction of liver fibrosis stage in blood donors and hepatitis C patients. We propose a method based on a selection of machine learning classification methods including multilayer perceptron (MLP) neural network, Naive Bayesian (NB), decision tree, and deep learning. Initially, the synthetic minority oversampling technique (SMOTE) is performed to address the imbalance in the dataset. Afterward, the integration of MLP and TLBO is implemented. We propose a novel algorithm that reduces the number of required patient features to seven inputs. The accuracy of MLP using 12 features is 0.903, while that of the proposed MLP with TLBO is 0.891. Besides, the diagnostic accuracy of all methods, except the model designed with the Bayesian network, increases when the SMOTE balancer is applied. The decision tree-based deep learning methods show the highest levels of accuracy with 12 features. Interestingly, with the use of TLBO and seven features, MLP reached an accuracy rate of 0.891, which is quite satisfactory when compared with those of similar studies. The proposed model provides high diagnostic accuracy, while reducing the required number of properties from the samples. The results of our study show that the recruited algorithm of our study is more straightforward, with a smaller number of required properties and similar accuracy.

Optimum Cropping Pattern in the Command Area of Nyari-2 Reservoir Using Teaching Learning-Based Optimization Algorithm

Optimum Cropping Pattern in the Command Area of Nyari-2 Reservoir Using Teaching Learning-Based Optimization Algorithm

Optimizing the series cascade control structure for nonminimum phase system regulation

AbstractThis work elucidates the control of integrating a nonminimum phase system via a series cascade scheme with fractional‐order P.I. (Proportional–Integral) plus D (Derivative) controller. The traditional Internal Model Control (IMC) is adopted for inner loop controller design. The feedback D controller is synthesized with the outer loop process model, showing the proposed work's universality. The outer loop controller is suggested in the IMC framework after the accountability of fractional‐filter and inverse response compensator. This combination is revealed to enhance performance without compromising robustness. The Riemann sheet principle is explored to compute the stability of the suggested controller. The sensitivity analysis has asserted the robustness. More importantly, the optimal value of controller settings is achieved via the Teaching Learning Based Optimization (TLBO) algorithm. This TLBO algorithm uses an objective function that minimizes Integral Square Error. Two illustrative problems are utilized to examine the recommended control structure's virtue.

DeepFore: A Deep Reinforcement Learning Approach for Power Forecasting in Renewable Energy Systems

An open network known as the “energy internet” links every component of the whole energy supply chains, from the generations. Due to their ability to mimic regional flow dynamics that have an impact on wind farm production, regional meteorological models are increasingly being used as a general tool for wind resource forecasting. In this study, higher vertical and horizontal resolutions WRF (weather research and forecasting) paradigm simulation are used to anticipate and validate production for a genuine onshore wind farm. This paper proposed a DeepFore which is a power forecasting system for hybrid renewable energy systems. Initially, the dataset is generated by the hybrid system. This data is preprocessed to improve the quality of the data by incorporating, filtering and outlier detection techniques. Then, this enriched data is fed into K++ means clustering algorithm to separate the normal data from faulty data. With the normal and original data, Teaching-Learning based optimization algorithm attempts to realize the optimal features which are important for forecasting. Finally, Deep SARSA which is deep reinforcement learning algorithm is incorporated to determine the power generated by the hybrid system. Better winds energy prediction estimations enable more efficient utilization of the produced electricity, according to computational models.