Learning Based Optimization Technique Research Articles

Mathematical Modelling Of Municipal Solid Waste Management In Spherical Fuzzy Environment

A Spherical fuzzy model induced with teaching learning based optimization technique is developed for supporting the municipal solid waste management under fuzzy environment. Spherical fuzzy set’s ability to capture imprecise and contradictory information results in a substantial contribution to decision-making issues. Thus, we introduce SFLPP in a spherical fuzzy environ-ment in this article, which entails maximization of truthiness and minimization of indeterminacy and falsity membership functions.In present era TLBO is gaining the popularity of being less complex and only two algorithmic parameters based algorithm. This study introduced a mathematical model to include all of the major components of municipal solid waste management. To deal with uncertainty, the mathematical model of municipal solid waste management is defined using a spherical fuzzy environment.The goal of this research is to determine the current state of waste management in the Dinanagar area of Punjab, India. Finally,the mathematical model is in possession of long-term waste management in the study area, Dinanagar city in Punjab, India. The findings of comparing the suggested model to the current framework show that the new model provides better solutions in terms of sustainability.

An efficient LightGBM-based differential evolution method for nonlinear inelastic truss optimization

A metaheuristic-based structural optimization method, whilst being popularly adopted due to its advantages in by-passing gradient function calculations, requires the use of time-consuming advanced analyses for constraint evaluation. To overcome this drawback, the present paper proposes a robust (machine learning-based) optimization method that combines the light gradient boosting machine (LightGBM) with the efficient p-best differential evolution (EpDE) method. In essence, the LightGBM classification model is constructed to assess the constraint (safety and integrity) satisfaction of structures. An efficient framework using a so-called safety parameter is proposed to prevent inaccurate predictions of the LightGBM model. The EpDE processes the optimization procedures on the constructed classification LightGBM model. This enables an enhanced machine learning-based optimization technique that not only maintains the sufficiently accurate optimal design of structures but also significantly reduces the required computing efforts, as compared to standard optimization schemes. Various examples of steel structure designs (i.e., two of which have been provided herein) have been successfully performed by the proposed approach. These illustrate the accuracy and robustness of the proposed method, where good comparisons with reference algorithms (including standard DE with “DE/rand/1” mutational strategy, Jaya, Rao-1 and CaDE) are evidenced. The statistical values collected present the accuracy and reliability of the proposed method in obtaining the minimum total weight designs with substantial (some 60%) reduction of computing efforts required to furnish optimal results.

A highly sensitive multi-channel SPR-PCF based biosensor with deep learning prediction approach

A very sensitive multi-channel refractive index-based biosensor with a detection range of 1.26 to 1.36 has been proposed. Titanium oxide (TiO2) was utilized as the dielectric, and gold as the plasmonic material in the sensor. The suggested sensor achieved a maximum sensitivity value of 48,000 nm/RIU and 7220 RIU-1 using the wavelength and amplitude interrogation techniques with a pitch layer variation of 5.55–5.95 μm and a metal layer variation of 30–50 nm. The suggested sensor can detect many compounds with amazing sensitivity, such as glucose, sucrose, ethanol, methanol, human intestinal mucosa, sevoflurane, and bio-chemicals. Additionally, a deep learning model that forecasts six optical properties has been proposed. The hyperparameters of the deep neural network model are tuned extensively to maximize accuracy. Our model had a Mean Squared Error of 0.006 and was significantly faster than conventional techniques. The results show that deep learning can predict optical features in photonic sensors, which can identify specific analytes or biomolecules in a sample without costly and time-consuming simulations. Photonic sensor researchers and engineers may utilize the suggested model on vast datasets and adjust it to new settings. This study may open the door to the use of deep learning-based optimization techniques to enhancebiosensors.

K-NN Query Optimization for High-Dimensional Index Using Machine Learning

In this study, we propose three k-nearest neighbor (k-NN) optimization techniques for a distributed, in-memory-based, high-dimensional indexing method to speed up content-based image retrieval. The proposed techniques perform distributed, in-memory, high-dimensional indexing-based k-NN query optimization: a density-based optimization technique that performs k-NN optimization using data distribution; a cost-based optimization technique using query processing cost statistics; and a learning-based optimization technique using a deep learning model, based on query logs. The proposed techniques were implemented on Spark, which supports a master/slave model for large-scale distributed processing. We showed the superiority and validity of the proposed techniques through various performance evaluations, based on high-dimensional data.

Machine learning assisted cancer cell detection using strip waveguide Bragg gratings

An early cancer diagnosis has become necessary in the medical domain as it can facilitate a timely and appropriate treatment, providing for the clinical management of patients. This work proposes a rapid and inexpensive label-free bio-sensing platform based on Waveguide Bragg Gratings for cancer detection. The refractive index values of cancer cells are different from normal cells, which provide a different reflection spectrum when the sample is placed on the functionalized surface of the waveguide grating structure. We combine simulations from the effective index method, finite element method, and Transfer Matrix method of the Grating structure to optimize the sensing structure and employ a neural network model to predict whether a sample contains cancerous cells. The machine learning model provides excellent accuracy for high-resolution interrogator data, and the accuracy falls below 95% only when the resolution of the interrogator is taken as 0.9 nm, thereby eliminating the need for an expensive high-resolution optical Interrogator system. This, combined with the mass-scale production capability of the waveguide Bragg gratings using standard CMOS technology, paves the way towards combining nano-scale optical biosensing platforms and machine learning-based optimization techniques for early and low-cost label-free cancer detections.

Prediction of rock drillability using gray wolf optimization and teaching–learning-based optimization techniques

Prediction of rock drillability using gray wolf optimization and teaching–learning-based optimization techniques

A new hybrid meta-heuristic optimization method for predicting UTS for FSW of Al/Cu dissimilar materials

Joining of aluminum (Al) and copper (Cu) materials used in aerospace, automotive, chemistry, nuclear, electrical-electronic and energy sector with traditional fusion welding methods has some negative consequences. One of the major negativities is the serious decrease in the mechanical properties of the materials as a result of the high temperature that occurs during welding. Therefore, Friction Stir Welding (FSW) is of great importance in joining materials which are dissimilar and have low melting temperatures. In this study, a hybrid artificial neural network (ANN) model has been developed to predict the relationship between FSW parameters and ultimate tensile strength and to increase the prediction performance of ANN using heuristic algorithms by welding Al and Cu plates with FSW. The used hybrid approach consists of ANN and Particle Swarm Optimization/Teaching Learning Based Optimization techniques. The welding applications conducted with three different welding parameters. These parameters, rotation speed (525, 1025, 1525 rpm), welding speed (50, 75, 100 mm/min) and tool offset (0–0.75–1.5 mm) were determined as input parameters of the developed ANN model. Tensile and microhardness tests were applied to determine the mechanical properties in the experimental studies after welding process. In addition, Scanning Electron Microscope analysis and X-ray diffraction were used for microstructure and characterization of the fracture surface and the results obtained were interpreted and examined.

Investigation and multi-objective optimization of friction stir welding of AA7075-T651 plates

The present study investigates and comparatively evaluates different evolutionary algorithms for multi-objective optimization of friction stir welding (FSW) of AA7075-T651 plates. The FSW parameters are optimized for obtaining the joint with higher tensile strength, microhardness and lower surface roughness using particle swarm optimization, strength Pareto-based evolutionary algorithm II, differential evolution and teaching learning-based optimization techniques. The validation experiments showed better prediction accuracy with the SPEA II technique. This study finds maximum tensile strength and microhardness of 187.45 MPa and 142.47 HV, respectively, and minimum surface roughness of 15.93 µm for FSW joint when using a welding speed and tool rotation, of 40 mm/min and 1923 rpm, respectively. The FSW joint obtained at these optimized parameters showed the homogeneous material mixing with the equiaxed fine-grain distribution at the weld nugget with fewer voids, as confirmed by the scanning electron microscopic images with energy-dispersive X-ray spectroscopic analysis.

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

Terminal voltage prediction of Li-Ion batteries using Combined Neural Network and Teaching Learning Based Optimization algorithm

The static and dynamic model parameters are critical parameters for the accurate estimation of open-circuit voltage and the terminal voltage of a Lithium-Ion (Li-Ion) battery. This work mainly focuses on the investigation of an accurate method for predicting the open-circuit voltage and terminal voltage of Li-Ion batteries. This work proposes an Enhanced Neural Network and Teaching Learning-Based Optimization (ENN-TLBO) algorithm to predict the terminal voltage of a Li-Ion battery used in electric vehicle applications. In this work, the static model is obtained by using a neural network (NN) while the dynamic model parameters are predicted by using the Teaching Learning Based Optimization technique (TLBO). In the NN method, different network parameters are varied to predict the accurate State of Charge (SoC)-Open Circuit Voltage (OCV) relation of the battery. The proposed static models are validated by the performance metrics such as RMSE and R2, the best static model is identified by low Root Mean Square Error (RMSE) and high R2 value of the model. In recent times many optimization algorithms are reported in the literature to predict the dynamic model, which requires certain algorithm-specific tuning parameters that affects the performance of the algorithm. The proposed TLBO algorithm is implemented with lesser tuning parameters to predict the hysteresis constant of the dynamic model. The RMSE value of the predicted terminal voltage at different temperature profiles is calculated to validate the proposed method. The proposed method has several advantages such as fewer tuning parameters, simple to implement, and high accuracy.

Investigation of Tensile Behavior of Carbon Nanotube/Coir Fiber/Fly Ash Reinforced Epoxy Polymer Matrix Composite

ABSTRACT Fiber-reinforced composite materials are lightweight and can withstand heavy loading conditions. Reinforcement augments the strength of the composite material, which is assessed by its elastic modulus. An attempt is made to reinforce epoxy with Coir Fiber, Carbon Nanotube (CNT) and Fly-ash. Central Composite Design (CCD), a Response Surface Methodology (RSM) tool, which is a Design of Experiment (DOE) technique, is used to fabricate the experimental samples to study their tensile behavior. Analysis of variance (ANOVA) is employed to investigate the effect of reinforcement percentage of CNT, coir-fiber, and fly-ash on tensile behavior of composite. The ANOVA results follow the trend of the experimental values with a deviation of less than 10% in yield strength, tensile strength, and Young’s modulus. Two models (Artificial Neural Network and Multiple Linear Regression Model) originated resting on the regression equation to speculate the elastic modulus for various reinforcement parameters using the experimental data. The main objective is to optimize reinforcement parameters using both the models having a maximum elastic modulus of 2.602 GPa and 2.682 GPa, respectively, which is achieved by the teaching learning-based optimization technique. Furthermore, confirmation experiments validate the optimization process with an error of less than 4%.

Micro-Siting of Wind Turbines in an Optimal Wind Farm Area Using Teaching–Learning-Based Optimization Technique

Nowadays, wind energy is receiving considerable attention due to its availability, low cost, and environment-friendly operation. Wind turbines are rarely placed individually but rather in the form of a wind farm with a group of several wind turbines. The purpose of this research is to perform studies on wind turbine farms in order to find the best distribution for wind turbines that maximizes the produced power, hence minimizing the wind farm area. Wind Farm Area Optimization (WFAO) is performed for optimal placement of wind turbines using elitist teaching–learning-based optimization (ETLBO) techniques. Three different scenarios of wind (first is fixed wind direction and constant speed, second is variable wind direction and constant speed, and third is variable wind direction and variable speed) are considered to find the optimal number of turbines and turbine positioning in a minimized squared land area that maximizes the power production while minimizing the total cost. Other research carried out in the past was to find the optimal placement of the wind turbines in a fixed squared land area of 2 km×2 km. In the present study, WFAO–ETLBO algorithm has been implemented to get the optimal land area for the placement of the same number of turbines used in the past research. For Case 1, there is a significant reduction in land area by approximately 30.75%, 45.25%, and 51.75% for each wind scenario, respectively. For Case 2, the reductions in land area for three different wind scenarios are respectively 30.75%, 7.2%, and 7.2%. For Case 3, there is a reduction of 7.2% in land area for each wind scenario. It has been observed that the results obtained by the WFAO–ETLBO algorithm with a significant reduction in the land area along with optimal placement of wind turbines are better than the results obtained from the wind turbines placement in the fixed land area of 2 km×2 km.

Effective Realization of Multi-Objective Elitist Teaching–Learning Based Optimization Technique for the Micro-Siting of Wind Turbines

In this paper, the meta-heuristic multi-objective elitist teaching–learning based optimization technique is implemented for wind farm layout discrete optimization problem. The optimization of wind farm layout addresses the optimum siting among the wind turbines within the wind farm to accomplish economical, profitable, and technical features. The presented methodology is implemented with multi-objective optimization problem through different targets such as minimizing cost, power output maximization, and the saving of the number of turbines. These targets are investigated with some case studies of multi-objective optimization problems in three scenarios of wind (Scenario-I: fixed wind direction and constant speed, Scenario-II: variable wind direction and constant speed, and Scenario-III: variable wind direction and variable speed) for the optimal micro-siting of wind turbines in a given land area that maximizes the power production while minimizing the total cost. To check the effectiveness of the algorithm, firstly, the results obtained for the three different scenarios have been compared with past studies available in the literature. Secondly, the numbers of turbines have also been optimized by using teaching–learning based optimization. It has been observed that the proposed algorithm shows the optimal layouts along with the optimal number of turbines with minimum fitness evaluation. Finally, the concept of elitism has been introduced in the teaching–learning based optimization algorithm. It is proposed that if elitist-teaching–learning based optimization with elite size of 15% is used, computational expense can be significantly reduced. It can be concluded that that the results obtained by the proposed algorithm are more accurate and advantageous than others.

An adaptive teaching learning based optimization technique for feature selection to classify mammogram medical images in breast cancer detection

An adaptive teaching learning based optimization technique for feature selection to classify mammogram medical images in breast cancer detection

Elementary, my dear Zernike: model order reduction for accelerating optical dimensional microscopy

Dimensional microscopy is an essential tool for non-destructive and fast inspection of manufacturing processes. Standard approaches process only the measured images. By modelling the imaged structure and solving an inverse problem, the uncertainty on dimensional estimates can be reduced by orders of magnitude. At the same time, the inverse problem needs to be solved in a timely manner. Here we present a method of accelerating the inverse problem by reducing images to their elementary features, thereby extracting the relevant information and distinguishing it from noise. The resulting reduction in complexity allows the inverse problem to be solved more efficiently by utilize cutting edge machine learning based optimization techniques. By employing the techniques presented here, we are able to perform for highly accurate and fast dimensional microscopy.

Design and Implementation of an Optimized PID Controller for the Adaptive Cruise Control System

We have designed and implemented an optimized PID controller for an adaptive cruise control system in this paper. The mathematical model for a cruise control system has been developed, and it is observed that it is a nonlinear first-order model with dead time. The objective functions chosen for optimizing the PID controller are ITE, ITAE and ITSE. The design of the optimized PID controller is based on the particle swarm optimization technique and teacher learning-based optimization technique. The results are scientifically compared with the conventionally tuned PID and fuzzy-based controllers. The optimized Proportional Integral Derivative controller shows better performance than a conventional PID and fuzzy-based controller. The overshoot of the system has been reduced to 0% from 46%, and the rise time has been reduced to 0.6150 s. This is the new work in the literature that will be quite useful for the performance enhancement of the cruise control system.

Machine Learning-Based Optimization Technique for Forecasting the Solar Radiation

Solar radiation measurement determines how often electricity a given area absorbs from the sun. This light is the key source of energy for conversion into solar thermal and photovoltaic plants. The radiation incident is not stable and relies on the temperature records, contributing to intermittent activity and electricity supply changes. This justifies designing a method to forecast and estimate incident radiation to predict improvements in photovoltaic systems’ performance. In this paper, the support vector machine (SVM) based machine learning is proposed to improve solar radiation prediction accuracy. The designed system results are compared with existing models that predicted the radiation and the global solar radiation is predicted accurately with efficient.

Comparison Investigation into Power System Optimization and Constraint-Based Generator Load Scheduling Using Metaheuristic Algorithms

In this paper, a novel flower pollination algorithm (FPA) is implemented to solve the problem of combined economic emission dispatch (CEED) in the power system. The FPA is a new metaheuristic optimization technique, which takes a biological approach to flower pollination. The FPA mimics the characteristics of flower pollination according to the survival of the fittest concept. CEED represents a combination of the emission and economic dispatch functions, formulated into a single function using the penalty factor. In this paper, the effect of valve point loading in the power system network is considered to obtain minimum fuel cost, minimum emissions, and optimum power generation. The performance of the proposed algorithm is evaluated using two test systems, namely 10 and 14 generating units by contemplating the valve point loading effect as well as transmission loss. The results of the 10 and 14 system units are compared with a learning-based optimization technique to demonstrate the effectiveness of the FPA. The findings reveal that the proposed FPA gives better performance than other algorithms with minimum fuel cost and emissions.

Modeling and optimization of dead metal zone to reduce cutting forces in micro-milling of hardened AISI D2 steel

Improving machining performance with reduced power consumption is a big challenge for the manufacturer to reduce production cost. Since the dead metal zone (DMZ) directly affects the cutting forces, the present study aims to optimize the DMZ to reduce the cutting and thrust forces in the micro-milling of hardened AISI D2 steel using teaching–learning-based optimization technique (TLBO). Finite element model for DMZ geometry and mechanistic models for cutting and thrust forces are developed, integrated and estimated the cutting and thrust forces. The estimated forces are compared with experimental results and a good agreement found between them. In the next stage, process parameters (cutting speed and feed per tooth) and tool parameters (nose radius and rake angle) are optimized using TLBO technique to minimize DMZ geometry keeping the surface roughness (≤ 2 µm), tool wear (≤ 30 µm) and amplitude of cutter vibration (≤ 30 µm) as constraints. The optimal working condition is as follows: a spindle speed of 2225 rpm, a feed per tooth of 5.0 µm, and a nose radius of 7.6 µm and rake angle of 3.0°. Under the optimal working condition, side length of DMZ and DMZ angle is found as 13.8 mm and 5.74°, respectively, and the cutting and thrust forces are estimated as 3.27 and 2.37 N, respectively. These cutting and thrust forces are about 21.3–65.7 and 34.8–55.3%, respectively, less than the experimental results.

Reservoir bank slope stability prediction model based on BP neural network

Safety monitoring and stability analysis of high slopes are essential for construction of concrete dam in precipitous gorges or mountainous areas. The estimate of slope stability is a difficult engineering shortcoming with a number of variables. Thereafter, a hybrid model of Support Vector Regression (SVR) and Teaching–learning-based optimization technique (TLBO) is proposed to develop the predicting function. TLBO was used in obtaining the best SRV factors to improve the prediction accuracy. Few essential factors, such as the installation height of instruments, classification of rock masses, modulus of elasticity, the complete measuring time cycle, the excavation height of slope, the start measuring time, and the actual excavation height after measurement are considered as the input parameter, but the slope displacement is regarded as output. The outcomes showed SRV-TLBO a reliable hybrid accurate prediction of slope stability, then it was effectively used to the left abutment slope of Jinping I hydropower station located in Yalongjiang concrete dam reservoir as a novel method for this purpose.