Teaching Learning Based Optimization Research Articles

Enhancing Education and Teaching Management Through Data Mining and Support Vector Machine Algorithm

This study investigates the effectiveness of the SVM+TLBO approach for predicting student performance and evaluating learning abilities in educational settings. By integrating Support Vector Machines (SVM) with Teaching-Learning-Based Optimization (TLBO), the research aims to enhance predictive accuracy and efficiency compared to traditional methods, including Decision Trees, Ant Colony Algorithms, Clustering Algorithms, Convolutional Neural Networks (CNN), Neural Networks, Support Vector Machine (SVM) without Optimization. Results indicate that the SVM+TLBO model significantly outperforms these methods, providing robust predictions and valuable insights into student learning patterns. The findings underscore the importance of data-informed decision-making in education, enabling educators to identify at-risk students and tailor instructional strategies effectively. This research contributes to the growing field of educational data mining, highlighting the potential of advanced machine learning techniques to optimize educational outcomes and foster personalized learning experiences.

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.

A ranking improved teaching-learning-based optimization algorithm for parameters identification of photovoltaic models

Solar energy is an important clean energy source, primarily applied for photovoltaic (PV) power generation. The precise identification of PV system parameters is critical for system control and simulation, posing a challenge due to the models' non-linearity, implicitness, and multiple optimal properties. So, a ranking improved teaching-learning-based optimization (RITLBO) is developed in this work to solve the problem of identifying the parameters of the PV model. RITLBO is a meta-heuristic algorithm based on teaching-learning-based optimization (TLBO) that simulates classroom teacher-student interaction. In RITLBO, learners are classified into inferior and superior groups based on their fitness ranking. During the teacher phase, superior learners emulate the top three agents with the highest fitness for local search, while inferior learners engage in guided mutual learning for global search, effectively utilizing computing resources. In the learner phase, superior learners receive guided information, while inferior learners engage in broader information exchange, balancing exploration and exploitation. RITLBO and fourteen algorithms are used to identify the parameters for five different PV models to confirm that the RITLBO is effective. Statistical results and analysis demonstrate that RITLBO is accurate and reliable in identifying PV model parameters. RITLBO offers promising prospects in optimizing PV system parameters through its unique strategies.

Wind power forecasting with metaheuristic-based feature selection and neural networks

Accurate forecasting of wind power generation is crucial for ensuring a stable and efficient energy supply, reducing the environmental impact of energy production, and promoting a cleaner and more sustainable energy supply. Inaccurate forecasts can lead to a mismatch between wind power generation and energy demand, resulting in wasted energy, increased emissions, and reduced grid stability. Therefore, improving the accuracy of wind power generation forecasting is essential for optimizing energy storage and grid management, reducing the reliance on fossil fuels, decreasing greenhouse gas emissions, and promoting a more sustainable energy future. This study proposes an innovative approach to enhance wind power generation forecasting accuracy by leveraging the strengths of metaheuristic algorithms for feature selection and integrating them with Neural Networks (NN). Specifically, five distinct algorithms - Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Teaching-Learning-Based Optimization (TLBO), and Evolutionary Mating Algorithm (EMA) - are integrated with NN model to identify optimal feature subsets from a comprehensive dataset of 18 diverse features. The results show that the GA consistently outperforms other algorithms in selecting the most influential features, leading to improved precision in wind power predictions. Notably, the GA achieves the best root mean square error (RMSE) of 37.1837 and the best mean absolute error (MAE) of 18.6313, outperforming the other algorithms and demonstrating the importance of feature selection in improving the accuracy of wind power forecasting. This innovative framework advances the field of renewable energy forecasting and provides valuable insights into optimizing feature sets for improved predictions across diverse domains.

Enhancing Indoor Localization Accuracy in Wireless Sensor Networks: A Hybrid Approach Integrating Hierarchical Structure Poly Particle Swarm Optimization and Teaching–Learning-Based Optimization (HSPPSO-TLBO)

Background and Objective: Enhancing localization accuracy while minimizing development costs poses significant challenges in deploying and managing wireless sensor networks (WSNs). This paper presents an advanced algorithm for node localization in indoor environments, integrating sophisticated optimization techniques. The hybrid algorithm, HSPPSO-TLBO, combines Hierarchical Structure Poly Particle Swarm Optimization (HSPPSO) with Teaching– Learning-Based Optimization (TLBO). Methods: The proposed algorithm HSPPSO-TLBO aims to minimize the mean squared range error (MSRE) resulted by calculating internal distances between nodes using Received Signal Strength Indicator (RSSI). TLBO, with its robust global search capabilities, complements HSPPSO’s local search, preventing convergence to inappropriate local optima. HSPPSO-TLBO offers easy implementation and leverages the cost-free feature of RSSI, making it an attractive choice for enhancing localization precision. Results: Simulation results demonstrate the superior performance of HSPPSO-TLBO compared to other algorithms using different meta-heuristic optimization techniques. The outstanding performance of HSPPSO-TLBO is evident across various evaluation metrics, including localization error, localization rate, and simulation runtime. Conclusion: The proposed algorithm utilizing HSPPSO and TLBO is exceptionally effective in improving localization precision in indoor WSNs due to several key characteristics. These include the seamless integration and easy implementation of both HSPPSO and TLBO, along with the costfree advantage of using the RSSI technique. This combination makes the algorithm a highly functional solution for improving localization accuracy. other: N/A

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.

Comparative study of reservoir operations using TLBO, PSO and DE optimization techniques: an experiment on the Hirakud reservoir, Odisha, India

ABSTRACT With the enduring progression of high population density, social economy and requirements of water supply, irrigation and hydropower, the water resource scarcity problem has been exacerbated in Odisha. Hence, the judicial operation of the Hirakud reservoir, which is considered the lifeline of Odisha, has become appreciably essential. Among various optimization techniques, metaheuristic algorithms are advanced techniques which can be applied for the optimal operation of a water reservoir. In this work, three metaheuristic algorithm-based optimization techniques, the Particle Swarm Optimization (PSO), Differential Evolution (DE) and Teaching–Learning-Based Optimization (TLBO) algorithms, are applied for optimal water management of the Hirakud reservoir. From the result, it was found that the efficiency of TLBO for irrigation release during the non-monsoon period was 99.45% compared with PSO with an efficiency of 97.3% and DE with an efficiency of 95.6%. The efficiency of TLBO for hydropower generation was 99.6%, whereas for PSO it was 98.8% and for DE it was 98.5%. It is found from the above experiment that TLBO showed a better performance than PSO and DE optimization techniques for the water management of the Hirakud reservoir. These metaheuristic techniques will provide suitable guidance for reservoir operations at times when the presence of experts is a must but they may not be available.

Deep contextual reinforcement learning algorithm for scalable power scheduling

This article introduces a deep contextual reinforcement learning (DCRL) optimization algorithm to tackle the NP-hard power scheduling problem. The algorithm is based on a multi-agent simulation environment that decomposes the power scheduling problem into sequential Markov decision processes (MDPs). The simulation environment inherently corrects incorrect decisions and adjusts supply capacities so that the MDPs adhere to the optimization constraints. A deep reinforcement learning (DRL) model is trained on these MDPs to provide optimal solutions. Demonstrating its applicability and effectiveness, the proposed method is evaluated on various test systems with different unit numbers, constraints, and production cost functions. The experimental results show that the proposed method has better performance relative to alternative methods, such as binary alternative moth-flame optimization (BAMFO), binary particle swarm optimization (BPSO), teaching learning-based optimization (TLBO), new binary particle swarm optimization (NBPSO), binary learning particle swarm optimization (BLPSO), quasi-oppositional teaching learning-based algorithm (QOTLBO), binary-real-coded genetic algorithm (BRCGA), hybrid genetic-imperialist competitive algorithm (HGICA), three-stage priority list (TSPL), and hybridized evolutionary algorithm and sequential quadratic programming (HEPSQP). The novelties of the proposed method lie in its adaptability to longer planning horizons and linear dimensionality complexity, rendering it suitable for large-scale power systems.

Network reconfiguration and integration of distributed energy resources in distribution network by novel optimization techniques

Nowadays, electrical load demand in radial distribution networks (RDN) is continuously growing, therefore RDNs are facing some serious challenges like voltage violation and line losses. Since modern RDNs have reconfiguration capabilities, optimal network reconfiguration is preferred over the costly construction of new lines and cables. In addition, the optimal integration of distributed energy resources (DER) helps to decrease above mentioned network challenges. This paper presents an improved version of the radiality maintenance algorithm (IRMA) to solve the optimal reconfiguration problem using a meta-heuristics algorithm. It also proposes two different schemes of algorithms by the blending of a Genetic algorithm (GA) and Teaching learning-based optimization (TLBO) to solve the optimal DER integration problem, where blending enhances the search space of each scheme which helps to find global minima in less iterations and time, while existing algorithms get stuck in local minima with same number of searching agents. The proposed problems are solved by minimizing active power loss by the single objective function and the sum of active power loss, reactive power loss, and voltage deviation index by multi-objective function using the weighted sum method where all objectives are equally dealt with, and their sum is used as single fitness function for the optimization algorithm. Four case studies are simulated using different DER technologies to improve each objective function. The efficiency of the proposed IRMA and two newly developed schemes of optimization algorithms, named GA-TLBO and TLBO-GA, are tested on two IEEE benchmark RDN of 33 and 69 buses. The results validate the efficiency of proposed algorithms that remarkably minimize a single objective from 210.9800 kW in the base case to 58.8768 kW, whereas existing algorithms like GWO and HHO were able to only reduce it to 72.7861 kW and 72.900 kW for IEEE 33 bus RDN, respectively. Similarly, for IEEE 69 bus system, single objective is reduced from base case of 224.9917 kW to 36.2543 kW, while existing algorithms like QOTLBO and QOSIMBO were only able to reduce it to 71.6250 kW and 71 kW, respectively. Furthermore, in multi-objective functions, reactive power losses and voltage deviation are also significantly improved from their base values. After that, the results of improved algorithms are compared with those of existing algorithms in the literature review for a comprehensive evaluation, which proves that proposed algorithm schemes are much more efficient and stable for the proposed problem as well as for standard benchmark optimization functions.

Application of machine learning models in predicting discharge coefficient of side B-type piano key weir

Side weir is a hydraulic structure within a channel which is usually used to discharge excess water, to divert the flow, and to regulate water surface levels in rivers and irrigation and drainage networks. In general, piano key weirs (PKW) have been used as weirs perpendicular to the flow direction in straight channels. However, the use of the PKW as a side weir in the outer arch of the channels is a new approach to enhance the weir's performance. In this study, 289 tests were first performed on the B-type rectangular side piano key weir (RSPKW) at two arc angles of 30 and 120°. Then, Fuzzy Inference System (FIS), Adaptive Neuro-Fuzzy Inference System (ANFIS), ANFIS and Teaching Learning Based Optimization (TLBO), ANFIS and Grasshopper Optimization Algorithm (GOA), Extreme Learning Machine (ELM) and Outlier Robust ELM (ORELM) models were used to predict the weir discharge coefficient. The results showed that two optimization models of TLBO and GOA increased the accuracy of the ANFIS model. The results showed that the ANFIS-GOA model has accuracy of Root Mean Squared Error (RMSE) = 0.0361, Coefficient of determination (R2) = 0.9772, and Kling Gupta coefficient (KGE) = 0.9858. The ANFIS-TLBO, ANFIS, and FIS models were ranked, respectively. Also, the results showed that ELM and ORELM models have accuracy close to ANFIS-GOA and can be a suitable alternative for complex fuzzy models. According to the statistical analysis, it was found that the parameters of the ratio of weir height to flow depth at the upstream edge of weir (P/h1), arc angle (α), and the ratio of height of the foundation to the main channel width (pd/B) had the greatest role in the development of the models, respectively.

COMPARACIÓN DEL RENDIMIENTO DE VARIOS ENFOQUES DE SOFT COMPUTING EN EL CONTROL DE LA FRECUENCIA DE CARGA PARA SISTEMAS ELÉCTRICOS INTERCONECTADOS QUE COMBINAN FUENTES TÉRMICAS-HIDRÁULICAS Y TÉRMICAS-DIÉSEL

Interconnected power systems offer a solution for meeting the increasing demand for reliable and efficient electricity supply. However, maintaining stability in such networks, especially in the face of frequency fluctuations, presents a significant challenge. Effective load frequency control (LFC) optimization is vital for ensuring stability by mitigating the impact of these fluctuations resulting from various disturbances. This study introduces PID LFC optimization strategies applied to a thermal-hydro and thermal-diesel interconnected power system. Six optimization methods, including ACO, PSO, GA, TLBO, DE, and CIO, are explored using four cost functions. Random step load perturbations are applied to test the response consistency of the LFCs. Initial findings indicate that recently developed CIO outperforms other algorithms in achieving desired outcomes while maintaining competitive computational expenses. Specifically, the CIO-ITAE optimized LFC demonstrates improved resilience in addressing challenges in the interconnected power system. Keywords: Load Frequency Control, LFC, Ant Colony Optimization (ACO), Differential Evolution (DE), Teaching Learning based Optimization (TLBO),Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Cohort Intelligence Optimization (CIO).

Trapezoidal neutrosophic teaching learning-based optimization in enhancing accuracy of diabetes prognosis

Diabetes is one of chronic diseases in which blood glucose (sugar) level soar up high where human body are incapable to absorb it properly. It is important to have an appropriate diagnosis for proper management and treatment. The aim of this manuscript is to provide a more accurate diabetes prediction model through the new adaptive Trapezoidal Neutrosophic Teaching Learning-Based Optimization (TLBO) method. In order to address the inherent uncertainties and imprecisions in medical data, the suggested model makes use of the resilience of Trapezoidal Neutrosophic sets. The Trapezoidal Neutrosophic set theory provides a suitable basis for developing rule/knowledge-based systems in the medical field. The present investigation makes use of the dataset acquired from the Pima Indians Diabetes Database (PIDD) website, which has an extensive global collection of diabetes datasets. The performance of our model is evaluated against several existing methodologies, including Intuitionistic Neuro-Fuzzy System (INFS) Structure, Fuzzy Logic based Diabetes Diagnosis System (FLDDS), Fuzzy Verdict Mechanism (FVM) for Diabetes Decision, (Fuzzy Expert System) FES, and Hierarchical Neuro-Fuzzy Binary space partitioning System (HNFB-1). Quantitative analysis validates that proposed methodology achieves an exceptional predictive accuracy of 99.89 %, which is substantially higher than the comparative methodologies, namely INFS Structure (88.76 %), FLDDS (87.2 %), FVM for Diabetes Decision (85.03 %), FES (81.7 %), and HNFB-1 (78.26 %). These enhancements demonstrate show how well the suggested model works to lower diagnostic errors and increase dependability.

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.

Hybrid artificial neural network models for bearing capacity evaluation of a strip footing on sand based on Bolton failure criterion

This paper employs the Bolton failure criterion, incorporating strength-dilatancy relationships, to analyze the bearing capacity factor of a strip footing on dense sand. Utilizing finite element limit analysis (FELA) based on the lower and upper bound theorems, the study presents the results as average bound solutions. By using the Bolton model, the b parameter is first calibrated and found that it should be about 3.50 to align the ultimate bearing capacity (qu) from FELA to have a good agreement with that from experimental test results from previous studies. The influence of parameters relevant to the Bolton failure criterion is analysed, showing that an increase in relative density (DR) significantly affects the variation in the bearing capacity factor (Nγ) at higher Q values, while lower Q values inhibit dilatancy due to soil crushing. The width of the strip footing (B) has a decreasing effect on Nγ at higher Q values, and the unit weight (γ) changes minimally impact Nγ within the range of 16–22 kN/m3. Additionally, an increase in the critical state friction angle (ϕcv) consistently increases Nγ, highlighting its direct correlation with soil shear strength. A hybrid artificial neural network (ANN) model integrates machine learning with four optimization algorithms: Imperialist Competitive Algorithm (ICA), Ant Lion Optimization (ALO), Teaching Learning Based Optimization (TLBO), and New Self-Organizing Hierarchical Particle Swarm Optimizer with Jumping Time-Varying Acceleration Coefficients (NHPSO-JTVAC). Comparative rank analysis of hybrid ANN models based on the selection of the optimal number of hidden neurons demonstrates that the ANN-TLBO model excels in predicting the bearing capacity factor, achieving a score of 48. This conclusion is corroborated by an error heatmap matrix, which indicates a minimized percentage of error relative to other hybrid ANN models. Importance analysis identifies particle crushing strength (Q) as the most significant factor influencing the bearing capacity factor (Nγ).

Artificial intelligence based experimental investigation of underwater fiber laser transmission process during micro-channelling operation on PMMA

Industries that engage in laser-based material processing are increasingly turning to fiber lasers as one of the most effective laser systems available. Using a pulsed fiber laser system, an experimental investigation of underwater laser micro-channelling on hard to machine thick PMMA has been conducted in this research work. Submerged laser transmission cutting which is also known as underwater laser induced backside machining helps to generate clean kerf edge without presence of mushy region, which is very common during laser machining of thick PMMA with wavelength of near infrared region. Submerged conditions are used here for laser transmission micromachining in order to reduce adverse thermal effects and microcracking or charring inside the material. Pulse frequency, working power, and cutting speed have been selected as the input parameters. As machining responses, the depth of the micro-channel, the width of the kerf, and the width of the heat affected zone (HAZ) have been considered. To find the ideal parametric condition for simultaneously achieving numerous goals, Response Surface Methodology (RSM) and AI-based Teaching Learning-Based Optimisation (TLBO) have been used. The TLBO technique determined that the optimal laser machining settings are a power setting of 13.98 %, a pulse frequency of 50 kHz, and a cutting speed of 0.20 mm/sec. These parameters result in a minimum HAZ width of 4.1874 µm, a minimum kerf width of 22.3698 µm, and a maximum depth of cut of 37.1478 µm. Underwater laser processing reduces heat affected zone and redeposition surrounding micro-channels, creating a cleaner, finer structure.

Utilizing the ANFIS and teaching-learning-based optimization (TLBO) methods to predict the maximum depth of scouring at culvert outlets

ABSTRACT Excessive scouring at culvert outlets is a widespread issue that often leads to culvert failure. At present, there is an absence of a universally applicable model for accurately predicting the occurrence of local scouring at culvert outlets. In this paper, a comparison is made between the performance of existing empirical equations and the results obtained from two advanced methods: adaptive neuro-fuzzy inference system (ANFIS) and the teaching-learning-based optimization (TLBO). This paper presents the development of the TLBO method, based on the ANFIS approach, for simulating scouring at culvert outlets. The algorithms for supervised training are applied by utilizing data collected from published studies. The results illustrate that the ANFIS–TLBO model successfully predicts scouring depth at culvert outlets with higher accuracy in comparison to existing empirical formulas. Furthermore, the model exhibits a wider range of applicability, accommodating diverse conditions. By integrating the ANFIS approach and TLBO optimization, the model surpasses the limitations of traditional empirical equations, providing engineers with a more reliable tool for predicting scour depth at culvert outlets. Furthermore, the proposed model has the largest accuracy compared to the previously developed empirical models. Ultimately, it can be concluded that the hybrid ANFIS–TLBO is a robust approach for scour depth prediction downstream of culverts.

Solving water scarcity challenges in arid regions: A novel approach employing human-based meta-heuristics and machine learning algorithm for groundwater potential mapping

Addressing water scarcity challenges in arid regions is a pressing concern and demands innovative solutions for accurate groundwater potential mapping (GPM). This study presents a comprehensive evaluation of advanced modeling techniques to enhance the precision of GPM. This study, conducted in the Zayandeh Rood watershed, Iran, employed a spatial database comprising 16 influential factors on groundwater potential and data from 175 wells. This study introduced an innovative approach to GPM by enhancing the Random Forest (RF) algorithm. This enhancement involved integrating three metaheuristic algorithms inspired by human behavior: ICA (Imperialist Competitive Algorithm), TLBO (Teaching-Learning-Based Optimization), and SBO (Student Psychology Based Optimization). The modeling process used 70% training data and 30% evaluation data. Data preprocessing was performed using the multicollinearity test method and frequency ratio (FR) technique to refine the dataset. Subsequently, the GPM was generated using four distinct models, demonstrating the combined power of machine learning and human-inspired metaheuristic algorithms. The performance of the models was systematically assessed through extensive statistical analyses, including root mean squared error (RMSE), mean absolute error (MAE), area under the curve (AUC) for the receiver operating characteristic curve (ROC), Friedman tests, chi-squared tests, and Wilcoxon signed-rank tests. RF-ICA and RF-SPBO emerged as frontrunners, displaying statistically comparable accuracy and significantly outperforming RF-TLBO and the non-optimized RF model. The results of the GPM revealed the exceptional accuracy of RF-ICA, which exhibited a commanding AUC score of 0.865, underscoring its superiority in discriminating between different groundwater potential classes. RF-SPBO also displayed strong performance with an AUC of 0.842, highlighting its effectiveness in inaccurate classification. RF-TLBO and the non-optimized RF model achieved AUC values of 0.813 and 0.810, respectively, indicating comparable performance. The outcomes of this study provide valuable insights for policymakers, offering a robust framework for tackling water scarcity challenges in arid regions through precise and reliable groundwater potential assessments.

Aeroelastic Stability and Design of Laminated Composite Sandwich MRE Plates Using Surrogate Metaheuristics Optimization

Sandwich composite materials with elastomeric cores and high-strength face layers have widespread applications in various engineering sectors as passive damping structural materials. This study examines the dynamic properties of sandwich plate with laminated composite face layers and smart magnetorheological elastomer (MRE) core under the action of the aerodynamic and hygrothermal loads. The dynamic model based on Kirchhoff’s plate theory is employed and the equations of motion are derived using Hamilton’s principle. The flow conditions are assumed supersonic and modeled by the first-order piston theory. The natural frequencies and aerodynamic pressure distributions are obtained from finite element model. Upon validating the model, the effects of various factors such as moisture, temperature and plate geometry on the critical aerodynamic pressure for different boundary conditions and magnetic fields are studied. Using these parametric data, a regression model is developed using Back-Propagation Neural Network to predict the critical aerodynamic pressure as a function of effective geometrical parameters. In order to enhance the aerodynamic stability, the critical aerodynamic pressure is maximized by the optimal selection of plate aspect ratio, face layer ply orientation sequence and core layer thickness ratio via the modified teaching–learning-based optimization (MTLBO) technique employing the trained neural network-based surrogate model. The approach is very convenient and it has been found that nondimensional aerodynamic pressure along with loss factor has improved almost twice. In the sensitivity analysis study, it is noticed that changes in aspect ratio around the optimal point have a relatively drastic effect on the critical aerodynamic pressure at all the face layer ply configurations.

A New Latin Hypercube Sampling with Maximum Diversity Factor for Reliability-Based Design Optimization of HLM

This research paper presents a new Latin hypercube sampling method, aimed at enhancing its performance in quantifying uncertainty and reducing computation time. The new Latin hypercube sampling (LHS) method serves as a tool in reliability-based design optimization (RBDO). The quantification technique is termed LHSMDF (LHS with maximum diversity factor). The quantification techniques, such as Latin hypercube sampling (LHS), optimum Latin hypercube sampling (OLHS), and Latin hypercube sampling with maximum diversity factor (LHSMDF), are tested against mechanical components, including a circular shaft housing, a connecting rod, and a cantilever beam, to evaluate its comparative performance. Subsequently, the new method is employed as the basis of RBDO in the synthesis of a six-bar high-lift mechanism (HLM) example to enhance the reliability of the resulting mechanism compared to Monte Carlo simulation (MCS). The design problem of this mechanism is classified as a motion generation problem, incorporating angle and position of the flap as an objective function. The six-bar linkage is first adapted to be a high-lift mechanism (HLM), which is a symmetrical device of the aircraft. Furthermore, a deterministic design, without consideration of uncertainty, may lead to unacceptable performance during the manufacturing step due to link length tolerances. The techniques are combined with an efficient metaheuristic known as teaching–learning-based optimization with a diversity archive (ATLBO-DA) to identify a reliable HLM. Performance testing of the new LHSMDF reveals that it outperforms the original LHS and OLHS. The HLM problem test results demonstrate that achieving optimum HLM with high reliability necessitates precision without sacrificing accuracy in the manufacturing process. Moreover, it is suggested that the six-bar HLM could emerge as a viable option for developing a new high-lift device in aircraft mechanisms for the future.

Parallel Disassembly Sequence Planning Using a Discrete Whale Optimization Algorithm for Equipment Maintenance in Hydropower Station

In a hydropower station, equipment needs maintenance to ensure safe, stable, and efficient operation. And the essence of equipment maintenance is a disassembly sequence planning problem. However, the complexity arises from the vast number of components in a hydropower station, leading to a significant proliferation of potential combinations, which poses considerable challenges when devising optimal solutions for the maintenance process. Consequently, to improve maintenance efficiency and decrease maintenance time, a discrete whale optimization algorithm (DWOA) is proposed in this paper to achieve excellent parallel disassembly sequence planning (PDSP). To begin, composite nodes are added into the constraint relationship graph based on the characteristics of hydropower equipment, and disassembly time is chosen as the optimization objective. Subsequently, the DWOA is proposed to solve the PDSP problem by integrating the precedence preservative crossover mechanism, heuristic mutation mechanism, and repetitive pairwise exchange operator. Meanwhile, the hierarchical combination method is used to swiftly generate the initial population. To verify the viability of the proposed algorithm, a classic genetic algorithm (GA), simplified teaching–learning-based optimization (STLBO), and self-adaptive simplified swarm optimization (SSO) were employed for comparison in three maintenance projects. The experimental results and comparative analysis revealed that the proposed PDSP with DWOA achieved a reduced disassembly time of only 19.96 min in Experiment 3. Additionally, the values for standard deviation, average disassembly time, and the rate of minimum disassembly time were 0.3282, 20.31, and 71%, respectively, demonstrating its superior performance compared to the other algorithms. Furthermore, the method proposed in this paper addresses the inefficiencies in dismantling processes in hydropower stations and enhances visual representation for maintenance training by integrating Unity3D with intelligent algorithms.