Teaching Learning Based Optimization Research Articles

Evaluating machining performance of acrylonitrile-butadiene-styrene (ABS) based electrical discharge machining (EDM) electrodes fabricated by fused deposition modelling (FDM) followed by a novel metallization method

In the present study machining performance of a novel ABS P400 based EDM electrode is experimentally investigated. Three EDM parameters that is, current ( I), pulse on time ( Ton) and pulse off time ( Toff), each at three levels, are considered, and mild steel is machined using these electrodes according to the response surface methodology (RSM) based face-centred central composite design (FCCCD). Machining performances such as material removal rate ( MRR), tool wear rate ( TWR) and surface roughness ( SR) are measured. Experimental results are analysed using analysis of variance (ANOVA), response graphs and 3D surface plots. Current is found to be the dominating parameter for MRR, TWR and SR. The optimal combination of EDM parameter for multi-performance is determined using teaching-learning based optimization (TLBO) algorithm. The effectiveness of the new electrodes is established by comparing their machining performances with the already developed electrode at the same machining parameter setting. MRR resulting from the new electrode and also its TWR are found to be in good agreement with those of already produced electrodes. However, SR of the machined surface using new electrodes is found to be relatively higher.

Redefining teaching-and-learning-process in TLBO and its application in cloud

Teaching-Learning-Based Optimization (TLBO) algorithm was developed to solve single-objective optimization problems. TLBO is inspired by the theory of teaching-learning mechanism. Basic TLBO works better for unimodal problems but poorly for multi-model problems because of its poor exploration. To provide a fair exploration for solving complex optimization problems, we redefined the learning strategy to the basic TLBO. This newly redefined variant is called Multi-Teacher Teaching-Learning Based Optimization (MT-TLBO). The performance of MT-TLBO was tested on the latest optimization benchmark functions, CEC-C06, 2019. It is observed that the performance of MT-TLBO is superb comparatively. After that, it is simulated to solve workflow scheduling problems by minimizing the execution cost of the standard workflow and maximizing the workload on computing resources in a cloud environment. Finally, it is found that MT-TLBO produces minimal execution cost and a fair workload distribution on resources on standard benchmark workflow, comparatively.

Experimental investigations and optimization of weld bead characteristics during submerged arc welding using recycled steel slag as a flux

A novel methodology has been established which allows to recycle steel slag as a suitable flux for submerged arc welding. The flux produced using waste slag as a raw material has been applied to deposit beads on plates. The bead geometry is dependent on welding parameters; hence optimization of parameters is essential. In this cut throat competition, researchers are striving to minimize the cost of experimentation using various experimental and optimization techniques to reduce number of experiments for save time, money, and resources. In this article various techniques have been explored for better optimized results. The welding parameters have been optimized to obtain desired bead geometry using various optimization techniques such as genetic and teaching–learning-based optimization algorithm, and desirability. It was observed that the genetic algorithm technique provides better results having a minimum error of penetration, bead width, and reinforcement (i.e. 1.12%, 1.83%, and 2.46%, respectively), as compared with other techniques applied. It is further observed that flux manufactured from waste slag is capable to produce desired bead profile which is similar with that of the fresh flux. Recycled slag is capable to produce optimum bead geometry having maximum penetration of 11.52 mm, bead width 20.73 mm, and reinforcement 3.24 mm.

Processing of Triangular Profile on Inconel-825 Superalloy by Wire Electrical Discharge Machining and Corner Error Optimization Using Teaching Learning-Based Optimization Technique

Processing of Triangular Profile on Inconel-825 Superalloy by Wire Electrical Discharge Machining and Corner Error Optimization Using Teaching Learning-Based Optimization Technique

Special issue on “Optimal design and operation of energy systems”

Special issue on “Optimal design and operation of energy systems”

Hybrid optimization strategy for evaluating sustainable performance of the electron beam welding process

With the escalation of climate change, the practice of sustainable welding has become imperative in today's manufacturing industries. Sustainable welding practice via advanced modeling and optimization techniques is a recent idea that has highly been emphasized in various studies for the sustainable production of components. This work, therefore, assess the sustainable performance of the electron beam welding (EBW) process using a novel hybrid optimization search strategy (FL-AHP-TLBO) that optimizes two sustainable characteristics i.e. net energy (Enet) as ‘environmental consideration’ and depth of penetration (P), weldment area (WA), ultimate tensile strength (UTS) being three quality factors as ‘economic consideration’. The optimization approach combines soft computing fuzzy logic (FL) modelling with the teaching-learning-based optimization (TLBO) algorithm to simultaneously optimize these factors while an analytical hierarchy process (AHP) is used to determine the optimal weightage for each factor. The approach yields a minimum fitness value of − 0.39945 at optimum parameter setting V= 60 kV, I= 38 mA, S= 1200 mm/min, and O= 600 Hz. On validation, the methodology improves the sustainable measures by 1.115% for P, 2.5150% for WA, 7.0423% for Enet, and 2.5836% for UTS. The proposed methodology also was found simple and adequate with a faster convergence rate, higher consistency, and results in an overall improvement of 7.377% over the traditional optimization approaches.

Determination of the Optimum Operating Conditions and Geometrical Dimensions of the Plate Fin Heat Sinks Using Teaching-Learning-Based-Optimization Algorithm

Abstract Electronic devices must be effectively cooled for long-term reliability and safe operation. It is essential to determine operating conditions and optimum dimensions of cooling devices in terms of device weight, space, cost, and sound limits. Plate fin heat sinks (PFHSs) are frequently used for cooling electronic devices. Optimum thermal designs of PFHSs are explored in this study using a teaching-learning-based-optimization (TLBO) algorithm where entropy generation (S˙gen) minimization, profit factor (J) maximization, base plate temperature excess (θb) minimization, total mass (mass) minimization, and total volume (volume) minimization are the objective functions of the constrained single-objective optimization problems. For further investigations of the entropy generation minimization method, three different optimization problems are also studied: minimization of thermal resistance (Rth), minimization of pressure drop (ΔP), and minimization of pumping power (W˙pump). Each optimization problem is subjected to a constraint, namely, temperature excess of base plate temperature (θb) should be lower than 10 K. Four optimization variables are considered which are the number of plate fins (N), freestream velocity (Vf), the thickness of the fin (tfin), and height of the fin (Hfin). Optimum configurations belonging to the different optimization problems are compared, and the effect of each optimization variable on the objective functions is discussed in detail. It is found that one can obtain optimum operating conditions and geometrical dimensions of the PFHSs according to the design objective, i.e., minimum mass requirement, space limitation, minimum base plate requirement, etc. As a result, the optimum designs of the studied cases are different which are superior to each other in terms of design targets.

A multi-class teaching–learning-based optimization for multi-objective distributed hybrid flow shop scheduling

Distributed hybrid flow shop scheduling problem (DHFSP) has attracted some attention. In this study, DHFSP with sequence-dependent setup times is studied and a multi-class teaching–learning-based optimization (MTLBO) is proposed to minimize makespan and maximum tardiness simultaneously. A two-string representation is adopted. s classes are formed to improve search efficiency by implementing reward and punishment mechanism among them. Class evaluation is introduced and two teacher phases and one learner phase are applied in the evolution of each class. Elimination process acts on the worst class to avoid the waste of computing resource. A number of experiments are conducted and the computational results demonstrate that MTLBO is a very competitive method for DHFSP.

New Fuzzy-Heuristic Methodology for Analyzing Compression Load Capacity of Composite Columns

Predicting the mechanical strength of structural elements is a crucial task for the efficient design of buildings. Considering the shortcomings of experimental and empirical approaches, there is growing interest in using artificial intelligence techniques to develop data-driven tools for this purpose. In this research, empowered machine learning was employed to analyze the axial compression capacity (CC) of circular concrete-filled steel tube (CCFST) composite columns. Accordingly, the adaptive neuro-fuzzy inference system (ANFIS) was trained using four metaheuristic techniques, namely earthworm algorithm (EWA), particle swarm optimization (PSO), salp swarm algorithm (SSA), and teaching learning-based optimization (TLBO). The models were first applied to capture the relationship between the CC and column characteristics. Subsequently, they were requested to predict the CC for new column conditions. According to the results of both phases, all four models could achieve dependable accuracy. However, the PSO-ANFIS was tangibly more efficient than the other models in terms of computational time and accuracy and could attain more accurate predictions for extreme conditions. This model could predict the CC with a relative error below 2% and a correlation exceeding 99%. The PSO-ANFIS is therefore recommended as an effective tool for practical applications in analyzing the behavior of the CCFST columns.

OPTIMAL ENERGY CONSUMPTION AND COST PERFORMANCE SOLUTION WITH DELAY CONSTRAINTS ON FOG COMPUTING

Cloud computing plays an essential role in development of the Internet of Things, which provides data processing and storage services. Fog computing is the evolution of cloud computing, which helps provide solutions to cloud computing challenges such as latency, location awareness, and real-time mobility support. Fog computing fills the gap between the cloud and IoT devices within the close vicinity of IoT devices. So, computation, networking, storage, data management, and decision making occur along the path between the cloud and IoT devices. The automatic and intelligent management of fog node resources and achieving an effective scheduling policy in the computing model is a necessary requirement and will lead to the improvement of the overall performance of fog computing. Some optimization problems are modeled by mixed-integer nonlinear programming (MINLP). In this paper, a model, i.e. an MINLP optimization problem on fog computing, is designed. Our model has two goals: to increase Cost Performance as well as to reduce energy consumption. Cost Performance is the price, users are charged as benefit/revenue. In other words Cost Performance is defined as the ratio of the average data rate of each user to its cost. Then the exact mathematical method with the GAMS program was used to prove its logical process. In the next step, we solved the model with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing+GA (SA+GA), Teaching–Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), and random method. According to the TOPSIS comparison, the SA+GA method with a value of 0.23 is the best compared to other methods. Then GWO, GA, TLBO, PSO, and GOA methods are better, respectively.

Anomaly Detection in Social Media Texts Using Optimal Convolutional Neural Network

Social Networking Sites (SNSs) are nowadays utilized by the whole world to share ideas, images, and valuable contents by means of a post to reach a group of users. The use of SNS often inflicts the physical and the mental health of the people. Nowadays, researchers often focus on identifying the illegal behaviors in the SNS to reduce its negative influence. The state-of-art Natural Language processing techniques for anomaly detection have utilized a wide annotated corpus to identify the anomalies and they are often time-consuming as well as certainly do not guarantee maximum accuracy. To overcome these issues, the proposed methodology utilizes a Modified Convolutional Neural Network (MCNN) using stochastic pooling and a Leaky Rectified Linear Unit (LReLU). Here, each word in the social media text is analyzed based on its meaning. The stochastic pooling accurately detects the anomalous social media posts and reduces the chance of overfitting. The LReLU overcomes the high computational cost and gradient vanishing problem associated with other activation functions. It also doesn’t stop the learning process when the values are negative. The MCNN computes a specified score value using a novel integrated anomaly detection technique. Based on the score value, the anomalies are identified. A Teaching Learning based Optimization (TLBO) algorithm has been used to optimize the feature extraction phase of the modified CNN and fast convergence is offered. In this way, the performance of the model is enhanced in terms of classification accuracy. The efficiency of the proposed technique is compared with the state-of-art techniques in terms of accuracy, sensitivity, specificity, recall, and precision. The proposed MCNN-TLBO technique has provided an overall architecture of 97.85%, 95.45%, and 97.55% for the three social media datasets namely Facebook, Twitter, and Reddit respectively.

Optimum regulation of THD profile in multilevel inverter using parameter-less AI technique for electrical vehicle application

This article proposes investigation on harmonic profile improvement using a novel fitness function in the multiphase multilevel inverter. For the proposed study in this paper, three-phase, seven-level cascaded H-bridge (CHB) multilevel inverter (MLI) is considered. Modulation of the stepped waveform output of the MLI is done using selective harmonic elimination (SHE) method. Many algorithms are proposed for solving the set of nonlinear transcendental trigonometric equations for SHE methods. Teaching learning-based optimisation (TLBO) algorithm is a parameter less optimisation technique. Due to the lack of controlling parameters, the proposed algorithm is most robust among the family of artificial intelligence (AI) techniques. In this paper, an investigation is carried out on a novel fitness function proposed for controlling total harmonic distortion (THD) for the proposed inverter. It is observed that the proposed fitness function improves THD profile below IEEE standards. It is also observed that THD profile obtained through this method is far better than THD profile obtained through various proposed methods so far. The results of THD profile from the MATLAB Simulink simulations are verified experimentally using seven-level three-phase hardware controlled by Arduino MEGA 2560 low-cost controller.

Intelligent Networked Control of Vasoactive Drug Infusion for Patients with Uncertain Sensitivity

Abnormal high blood pressure or hypertension is still the leading risk factor for death and disability worldwide. This paper presents a new intelligent networked control of medical drug infusion system to regulate the mean arterial blood pressure for hypertensive patients with different health status conditions. The infusion of vasoactive drugs to patients endures various issues, such as variation of sensitivity and noise, which require effective and powerful systems to ensure robustness and good performance. The developed intelligent networked system is composed of a hybrid control scheme of interval type-2 fuzzy (IT2F) logic and teaching-learning-based optimization (TLBO) algorithm. This networked IT2F control is capable of managing the uncertain sensitivity of the patient to anti-hypertensive drugs successfully. To avoid the manual selection of control parameter values, the TLBO algorithm is mainly used to automatically find the best parameter values of the networked IT2F controller. The simulation results showed that the optimized networked IT2F achieved a good performance under external disturbances. A comparative study has also been conducted to emphasize the outperformance of the developed controller against traditional PID and type-1 fuzzy controllers. Moreover, the comparative evaluation demonstrated that the performance of the developed networked IT2F controller is superior to other control strategies in previous studies to handle unknown patients’ sensitivity to infused vasoactive drugs in a noisy environment.

Enhanced the Control Strategy of a Triple Link Robotic System (Robogymnast)

This research aims to optimise performance in a Fuzzy Linear Quadratic Regulator controller by implementing 2 types of algorithm to stabilise the triple-link ‘Robogymnast’ robotic system. The Robogymnast recreates the movements of a human gymnast, with the hand/arm element of the system securely attached to a high bar with ball0bearing mountings, which can rotate freely. This article explores the complex factors involved in swing-up control in the underactuated Robogymnast and presents a linearisation of the mathematical modelling for the system, considering methods which apply Lagrangian equations in defining state space in such systems. Both the Teaching-Learning-Based Optimization (TLBO) and Particle Swarm Optimization (PSO) algorithm were used for tuning the hybrid Fuzzy Linear Quadratic Regulator controller for later application with the robot and assessment of response stability. In addition, MATLAB Simulink was used to simulate performance and show the effect of altering parameters including time for rising, settling and overshoot. This work aims primarily to explore how a Linear Quadratic Regulator/Fuzzy Logic controller can be applied with acrobatic robots.

Modified reptile search algorithm with multi-hunting coordination strategy for global optimization problems.

The reptile search algorithm (RSA) is a bionic algorithm proposed by Abualigah. et al. in 2020. RSA simulates the whole process of crocodiles encircling and catching prey. Specifically, the encircling stage includes high walking and belly walking, and the hunting stage includes hunting coordination and cooperation. However, in the middle and later stages of the iteration, most search agents will move towards the optimal solution. However, if the optimal solution falls into local optimum, the population will fall into stagnation. Therefore, RSA cannot converge when solving complex problems. To enable RSA to solve more problems, this paper proposes a multi-hunting coordination strategy by combining Lagrange interpolation and teaching-learning-based optimization (TLBO) algorithm's student stage. Multi-hunting cooperation strategy will make multiple search agents coordinate with each other. Compared with the hunting cooperation strategy in the original RSA, the multi-hunting cooperation strategy has been greatly improved RSA's global capability. Moreover, considering RSA's weak ability to jump out of the local optimum in the middle and later stages, this paper adds the Lens pposition-based learning (LOBL) and restart strategy. Based on the above strategy, a modified reptile search algorithm with a multi-hunting coordination strategy (MRSA) is proposed. To verify the above strategies' effectiveness for RSA, 23 benchmark and CEC2020 functions were used to test MRSA's performance. In addition, MRSA's solutions to six engineering problems reflected MRSA's engineering applicability. It can be seen from the experiment that MRSA has better performance in solving test functions and engineering problems.

Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud Environment

Hybrid Teaching–Learning-Based Optimization for Workflow Scheduling in Cloud Environment

Inverse modeling application for aquifer parameters estimation using a precise simulation–optimization model

In this research, a simulation–optimization (S/O) model is used in order to estimate aquifer parameters on two aquifers. In this model, meshless local Petrov–Galerkin (MLPG) is used for simulation purpose and modified teaching–learning-based optimization (MTLBO) algorithm is engaged as optimization model. Linking these two powerful models generates a S/O model named MLPG-MTLBO. The proposed model is applied on two aquifers: a standard and a real field aquifer. In standard aquifer, parameters are only transmissivity coefficients in x and y direction for three zones. The acquired results by MLPG-MTLBO are really close to true values. This fact presents the power of MLPG-MTLBO inverse model. Therefore, it is applied on field aquifer. Unconfined aquifer of Birjand recognized as real case study. Parameters which are needed to be estimated are specific yields and hydraulic conductivity coefficients. These parameters are computed by MLPG-MTLBO and entered to the groundwater flow model. The achieved groundwater table compared with observation data and RMSE is calculated. RMSE value is 0.356 m; however, this error criterion for MLPG and FDM is 0.757 m and 1.197 m, respectively. This means that estimation is precise and makes the RMSE to reduce from 0.757 to 0.356 m, and also, MLPG-MTLBO is an accurate model for this aim.

Multi-objective train speed profile determination for automatic train operation with conscious search: A new optimization algorithm, a comprehensive study

Train speed profile is one of the most fundamental optimization issues in rail transportation. The speed profile has several indices, such as travel time and energy consumption. These indices mostly have opposite natures, which leads to a non-linear, complex multi-objective optimization problem. This paper introduces a new heuristic algorithm called Conscious Search (CS) to determine the optimal speed profile. For this purpose, after modeling the train dynamics, we initially analyze the validated heuristic optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bees Algorithm (BA), and Teaching–learning Based Optimization (TLBO) statistically using sensitivity analysis. Then, using new criteria called the Impact Factor, we show that a trade-off prevails in the global and local search for algorithms. In general, the stronger the algorithm in one domain, the weaker in the other. Subsequently, no algorithm is superior to another. In this regard, CS is proposed as a hybrid algorithm that performs the optimization in two separate global and local phases to address this problem and achieve the ideal optimization. CS steps are designed to dominate other algorithms in both local and global phases. In the evaluation, we obtained the train speed profile using the proposed method and the proven latest versions of the mentioned algorithms. Based on the simulation results, CS outperforms the considered algorithms by a palpable margin, and its solutions dominate others. These results indicate on effectiveness and excellence of the proposed method.

Optimized Approach for Localization of Sensor Nodes in 2D Wireless Sensor Networks Using Modified Learning Enthusiasm-Based Teaching–Learning-Based Optimization Algorithm

Wireless Sensor Networks (WSNs) have a wonderful potential to interconnect with the physical world and collect data. Data estimation, long lifespan, deployment, routing, task scheduling, safety, and localization are the primary performance difficulties for WSNs. WSNs are made up of sensor nodes set up with minimal battery power to monitor and reveal the occurrences in the sensor field. Detecting the location is a difficult task, but it is a crucial characteristic in many WSN applications. Locating all of the sensor nodes efficiently to obtain the precise location of an occurrence is a critical challenge. Surveillance, animal monitoring, tracking of moving objects, and forest fire detection are just a few of the applications that demand precise location determination. To cope with localization challenges in WSNs, there is a variety of localization algorithms accessible in the literature. The goal of this research is to use various optimization strategies to solve the localization problem. In this work, a modified learning enthusiasm-based teaching–learning-based optimization (mLebTLBO) algorithm is used to cope with a 2D localization problem applying the notion of an exclusive anchor node and movable target nodes. A modified LebTLBO algorithm seeks to increase overall efficiency by assessing the exploration and exploitation abilities. The computational results reveal that this technique outperforms others with respect to localization errors in a 2D environment of WSN.

An Efficient Hybrid Particle Swarm and Teaching-Learning-Based Optimization for Arch-Dam Shape Design

Particle swarm optimization is a popular meta-heuristic with highly explorative features; however, in its standard form it suffers from a poor convergence rate and weak search refinement on multi-dimensional problems. The present work improves the conventional particle swarm optimizer in three ways: adding a greedy selection for better intensification; embedding an extra movement borrowed from teacher–learner-based optimization; and utilizing a neighborhood strategy by averaging over a random half of the swarm. The performance of the proposed method is subsequently evaluated on three sets of problems. The first set includes uni-modal, multi-model, separable and non-separable test functions. The proposed method is compared with a standard particle swarm optimizer and its variants as well as other meta-heuristic algorithms. Engineering benchmark problems including the optimal design of a tubular column, a coiled spring, a pressure vessel and a cantilever beam constitute the second set. The third set includes constrained sizing design of a 120-bar dome truss and the optimal shape design of the Morrow Point double-arch concrete dam as a practical case study. Numerical results reveal considerable enhancement of the standard particle swarm via the proposed method to exhibit competitive performance with the other studied meta-heuristics. In the optimal design of Morrow Point Dam, the proposed method resulted in a material consumption 21 times smaller than the best of the initial population and 26% better than a recommended practical design.