Firefly, Teaching Learning Based Optimization and Kalman Filter Methods for GPS Receiver Position Estimation

Automatic generation control of multi-area power systems with diverse energy sources using Teaching Learning Based Optimization algorithm

Hippopotamus algorithm for optimal sizing of hybrid renewable energy system

An experimental investigation and optimization of energy consumption and surface defects in wire cut electric discharge machining

In continually increasing area and structure of modern power system having burden demand uncertainties, the use of knowledgeable and vigorous frequency power strategy is essential for the satisfactory functioning of the Power system. A combined fuzzy proportional-integral-derivative (CFPID) controller is suggested for frequency supervision of the power system. To optimize the controller parameters, a review of sine and cosine work adjusted improved whale optimization algorithm (SCiWOA) has been utilized. The next practical application of power-system frequency control is performed by designing a CFPID controller using the proposed SCiWOA technique for a smart grid system having inexhaustible sources like sun oriented, wind, photovoltaic and capacity gadgets like a battery, flywheel just as module electric vehicles. The first advantages of the SCiWOA tuned CFPID controller over hybrid-particle-swarm-optimization and pattern-search (hPSO-PS) adjusted fuzzy proportional-integral (FPI) controller, hybrid bacterial foraging optimization algorithm-particle swarm optimization (hBFOA-PSO) adjusted proportional-integral (PI) controller, genetic algorithm (GA) tuned proportional and integral (PI) controller, BFOA adjusted PI controller, jaya algoritm (JA) tuned PID with derivative filter (PIDN) controller and teaching learning based optimization (TLBO) tuned proportional-integral-derivative (PID) controller are demonstrated for the two-area non-reheat thermal power system. The second advantages of the SCiWOA tuned CFPID controller over artificial-bee-colony (ABC) tuned PID controller, SOSA tuned PID controller and Firefly algorithm (FA) tuned PID controller are demonstrated for two-area reheat thermal power system. It is seen that SCiWOA based CFPID controller is more effective in controlling the recurrence comparative with PID regulator.

Wireless sensor networks (WSNs) are composed of sensor nodes, having limited energy resources and low processing capability. Accordingly, major challenges are involved in WSNs Routing. Thus, in many use cases, routing is considered as an NP-hard optimization problem. Many routing protocols are based on metaheuristics, such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). Despite the fact that metaheuristics have provided elegant solutions, they still suffer from complexity concerns and difficulty of parameter tuning. In this paper, we propose a new routing approach based on Teaching Learning Based Optimization (TLBO) which is a recent and robust method, consisting on two essential phases: Teacher and Learner. As TLBO was proposed for continuous optimization problems, this work presents the first use of TLBO for the discrete problem of WSN routing. The approach is well founded theoretically as well as detailed algorithmically. Experimental results show that our approach allows obtaining lower energy consumption which leads to a better WSN lifetime. Our method is also compared to some typical routing methods; PSO approach, advanced ACO approach, Improved Harmony based approach (IHSBEER) and Ad-hoc On-demand Distance Vector (AODV) routing protocol, to illustrate TLBO's routing efficiency.

This paper focuses on the optimal power flow solution and the enhancement of the performance of a power system network. The paper presents a secured optimal power flow solution by integrating Thyristor controlled series compensator (TCSC) with the optimization model developed under overload condition. The Teaching Learning Based Optimization (TLBO) has been implemented here. Recently, the opposition-based learning (OBL) technique has been applied in various conventional population based techniques to improve the convergence performance and get better simulation results. In this paper, opposition-based learning (OBL) has been integrated with teaching learning based optimization (TLBO) to form the opposition teaching learning based optimization (OTLBO). Flexible AC Transmission System (FACTS) devices such as Thyristor controlled series compensator (TCSC) can be very effective for power system security. Numerical results on test systems IEEE 30-Bus with valve point effect is presented and compared with results of other competitive global approaches. The results show that the proposed approach can converge to the optimum solution and obtains the solution with high accuracy.

A hybrid Many Optimizing Liaisons Gravitational Search Algorithm (hMOL-GSA)-based fuzzy PID controller is proposed in this work for Automatic Generation Control problem. MOL is a simplified version of particle swarm optimization which ignores the particle best position consequently simplifying the algorithm. The proposed method is employed to tune the fuzzy PID parameters. The outcomes are equated with some newly proposed methods like Artificial Bee Colony (ABC)-based PID for the identical test systems to validate the supremacy of GSA and proposed hMOL-GSA techniques. Further, the design task has been carried out in a three-area test system and the outcomes are equated with newly proposed Firefly Algorithm (FA) optimized PID and Teaching Learning-Based Optimization (TLBO) tuned PIDD controller for the identical system. Better system response has been observed with proposed hMOL-GSA method. Finally, sensitivity study is being carried out and robustness of the proposed method is established.

Growing concern about limiting the environmental issues of materials allowed distinguished researchers to employ natural fiber-reinforced green composites suitable for industrial applications. Conventional machining methods are not best suited to machining green composites due to hole deformation, delamination, thermal damage, debonding, interlaminar cracking, burrs, etc. Abrasive water jet machining (AWJM) process is employed which limits cracks or thermal damage, prevents burrs in the product and is environmentally friendly. The inhomogeneous distributions and orientation of filler-matrix and anisotropic behavior affected the surface quality of machined composites. Taguchi https://www.w3.org/1998/Math/MathML" display="inline"> L 27 https://www.w3.org/1999/xlink" xlink:href="https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781032661858/3ddbfb91-6bba-40fc-913c-3d666fbefe3d/content/ieq0001.tif"/> experiments were carried out to gain detailed insights into parametric influences [abrasive material grain size (AMGS), standoff distance (SOD), nozzle speed (NS), abrasive mass flow rate (AMFR), and working pressure (WP)] on surface roughness of micron-bamboo fiber-reinforced polymer (MBFRP) machined components. SOD significantly impacted surface roughness (SR), followed by NS, AMFR, WP, and AMGS. The empirical equation derived for SR was expressed as a mathematical function of AWJM parameters. The model produced a good coefficient of determination equal to 90.78%, signifying a better goodness-of-fit useful for prediction and optimization. Three algorithms [teaching learning-based optimization (TLBO), artificial bee colony (ABC), and particle swarm optimization (PSO)] were applied to determine the best parametric conditions that improve the surface finish of machined composites. All three algorithms predicted identical optimal conditions (60 mesh of AMGS, https://www.w3.org/1998/Math/MathML" display="inline"> 1.5 mm https://www.w3.org/1999/xlink" xlink:href="https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781032661858/3ddbfb91-6bba-40fc-913c-3d666fbefe3d/content/ieq0002.tif"/> of SOD, https://www.w3.org/1998/Math/MathML" display="inline"> 250 mm/min https://www.w3.org/1999/xlink" xlink:href="https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781032661858/3ddbfb91-6bba-40fc-913c-3d666fbefe3d/content/ieq0003.tif"/> of NS, https://www.w3.org/1998/Math/MathML" display="inline"> 3 gm/s https://www.w3.org/1999/xlink" xlink:href="https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781032661858/3ddbfb91-6bba-40fc-913c-3d666fbefe3d/content/ieq0004.tif"/> of AMFR, and https://www.w3.org/1998/Math/MathML" display="inline"> 100 MPa https://www.w3.org/1999/xlink" xlink:href="https://s3-euw1-ap-pe-df-pch-content-public-p.s3.eu-west-1.amazonaws.com/9781032661858/3ddbfb91-6bba-40fc-913c-3d666fbefe3d/content/ieq0005.tif"/> of WP) and resulted experimentally in a minimum surface roughness value equal to 0.135 μm. PSO requires less computation time than TLBO and ABC.

Pressure management and optimal placement of pressure reducing valves (PRVs) play a critical role in minimizing leakage and maintaining operational efficiency in water distribution networks (WDNs). While evolutionary and swarm-based algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) have demonstrated promising results for PRV configuration, their efficiency is often contingent on careful parameter tuning and can be limited by convergence sensitivity or complex network scenarios. This study systematically evaluates the performance of the teaching-learning-based optimization (TLBO) algorithm – a population-based, parameter-free metaheuristic – for PRV placement and setting adjustment. Exploiting TLBO's teacher and learner phases, the approach achieves robust, repeatable leakage reduction without the need for intensive algorithm customization. Comparative results on a benchmark WDN confirm that TLBO not only minimizes the number of objective function evaluations but also consistently identifies PRV configurations that outperform or match those found by GA, PSO, and other established methods. The findings underline TLBO's practical advantages and highlight future opportunities to combine intelligent pipe selection strategies and real-time adaptive PRV control for further improvement in leakage management.

Teaching---learning-based optimisation (TLBO) is an emerging gradient-free optimisation algorithm inspired by interactions between students and teacher in classrooms. TLBO has no control parameter to be tuned by user. This property makes it popular in research community. It has been successfully applied to challenging optimisation problems in different areas. In this study, TLBO is assisted to find optimal setting of thyristor-controlled series compensators in electric power systems. The experiments have been done for both N-1 and N-2 line outage contingencies. The results show that TLBO performs well in solving this problem.

Hastelloy C276 is renowned for its exceptional resistance to corrosion and elevated temperatures, rendering it a preferred material for aerospace and chemical processing applications. However, its high strength and work-hardening tendency pose significant challenges during machining. This study systematically investigates the machinability of Hastelloy C276 under four sustainable lubrication and cooling environments—dry machining, minimum quantity lubrication (MQL), nano-enhanced MQL (NMQL), and cryogenic CO₂ (CCO₂). Experiments were designed using a Taguchi L16 orthogonal array, and the influence of cutting speed and feed rate on surface roughness, cutting force, tool wear, and cutting temperature was analysed using ANOVA. Compared to dry machining, cryogenic CO₂ cooling resulted in a reduction of surface roughness and cutting force by approximately 30–40%, along with a substantial decrease in tool wear and cutting temperature, whereas NMQL demonstrated moderate improvements due to enhanced lubrication at the tool–chip interface. ANOVA results revealed that feed rate and cutting speed were the most significant parameters, contributing up to 38.35% and 48.56% to variations in surface roughness and cutting temperature, respectively. To identify optimal machining conditions, Particle Swarm Optimization (PSO) and Teaching–Learning-Based Optimization (TLBO) algorithms were employed. Over 100 iterations, PSO achieved a higher optimization success rate of 83.6% compared to 79.1% for TLBO, while TLBO exhibited faster convergence with a computation time of 6.5 s against 9 s for PSO. The findings demonstrate that cryogenic CO₂-assisted machining combined with evolutionary optimization provides an effective and sustainable strategy for enhancing the machinability of Hastelloy C276.

Examination of retina and its associated parts, such as optic-nerve, cup, and disc are widely preferred in Ophthalmology to identify the retinal abnormalities and to plan/implement appropriate treatment procedure to cure the disease. This paper implements a Machine-Learning (ML) technique to extract the Optic Disc/Cup from the Benchmark-Fundus-Image-Dataset (BFID). The proposed ML technique is implemented by integrating the Social-Group-Optimization (SGO) monitored Kapur's thresholding and Level-Set-Segmentation (LSS). In this work, the BFID, such as RIM-ONE and IDRiD are chosen for the examination and the essential retinal parts such as Optic Disc/Cup are extracted using the proposed ML technique. After mining the essential part from the chosen BFID, the advantage of the proposed approach is then appraised by employing a relative analysis among mined retinal section and its associated ground-truth. Further, the performance of SMOA is validated against the Bacterial-Foraging-Optimization (BFO), Teaching-Learning-Based-Optimization (TLBO) and Firefly-Algorithm (FA) existing in the literature. The result of this work validates that, SGO based practice is capable in investigative the BFID pictures with significant image similarity measures.

Time varying acceleration coefficients particle swarm optimisation (TVACPSO): A new optimisation algorithm for estimating parameters of PV cells and modules

Medical image segmentation using exchange market algorithm

Accurate and efficient disease diagnosis plays a crucial role in healthcare. This study proposes a novel approach to enhance disease diagnosis performance by combining bagging and Teaching-Learning-Based Optimization (TLBO). The objective is to develop an optimized ensemble model that leverages the strengths of multiple base classifiers to improve diagnostic accuracy. The proposed methodology involves several key steps. TLBO optimization process is employed to dynamically select the most informative instances (bags) from the training data. The optimization process iteratively refines the bags by considering the fitness of each ensemble model constructed using different base classifiers. The soundness of an ensemble model is evaluated based on its accuracy in predicting the target variable. To further enhance the performance of the base classifiers, hyperparameter tuning using grid search is incorporated into the model training process. This ensures that each base classifier is optimized with the best set of hyperparameters, leading to more accurate predictions. The optimized bags are then used to train the base classifiers, which include Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Gaussian Naive Bayes (GNB). The base classifiers are combined into ensemble models using a Voting Classifier, allowing them to complement each other’s strengths and improve overall prediction performance. The results indicate that the TLBO-optimized ensemble models outperform individual base classifiers and traditional ensemble methods. The diversity among classifiers plays a crucial role in influencing the performance of ensemble models, especially in the context of disease diagnosis. Evaluating dissimilarity measures between classifiers becomes a key strategy in disease diagnosis. The dynamically selected bags contribute to improved accuracy, as they contain the most relevant instances for disease diagnosis and also explore computation time and diversity proposed ensemble approach on disease datasets.