After the teaching–learning-based optimization (TLBO) algorithm was proposed, many improved algorithms have been presented in recent years, which simulate the teaching–learning phenomenon of a classroom to effectively solve global optimiz...
The Teaching-Learning-Based Optimization (TLBO) algorithm has been proposed in recent years. It is a new swarm intelligence optimization algorithm simulating the teaching-learning phenomenon of a classroom. In this paper, a novel global path planning...
Accurate travel route optimization is essential to promote and grow tourism in modern society. This paper investigates a travel route optimization problem alongside the urban railway line and proposes a hybrid teaching–learning-based optimizati...
This paper presents a novel variant of the teaching–learning-based optimization algorithm, termed BLTLBO, which draws inspiration from the blended learning model, specifically designed to tackle high-dimensional multimodal complex optimization...
Teaching–learning-based optimization has the disadvantages of weak population diversity and the tendency to fall into local optima, especially for multimodal and high-dimensional problems such as the optimal reactive power dispatch problem. To...
The bulk of water pipes experience major degradation and deterioration problems. This research aims at estimating the condition of water pipes in Shattora and Shaker Al-Bahery’s water distribution networks, in Egypt. The developed models involv...
Nowadays, since large amounts of energy are consumed for a variety of applications, more and more emphasis is placed on the conservation of energy. Recent investigations have experienced the significant advantages of using metaheuristic algorithms. G...
Convolutional neural networks (CNNs) have exhibited significant performance gains over conventional machine learning techniques in solving various real-life problems in computational intelligence fields, such as image classification. However, most ex...
This paper proposes an optimal design method to suppress critical harmonics and improve the power factor by using passive power filters (PPFs). The main objectives include (1) minimizing the total harmonic distortion of voltage and current, (2) minim...
In today’s competitive and rapidly evolving manufacturing environment, optimizing the composition of manufacturing services is critical for effective supply chain deployment. Since the manufacturing environment involves many two-fold uncertaint...
Aiming to effectively recognize train center plate bolt loss faults, this paper presents an improved fault detection method. A multi-scale local binary pattern operator containing the local texture information of different radii is designed to extrac...
Given the pressing need to strengthen operational reliability in electrical distribution networks, this study proposes an optimization methodology based on the Teaching–Learning-Based Optimization (TLBO) algorithm for the strategic location of...
The present study proposes a maximum power point tracking (MPPT) method in which improved teaching-learning-based optimization (I-TLBO) is applied to perform global MPPT of photovoltaic (PV) module arrays under dissimilar shading situations to ensure...
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....
Energy-efficient scheduling problems with additional resources are seldom studied in hybrid flow shops. In this study, an energy-efficient hybrid flow shop scheduling problem (EHFSP) with additional resources is studied in which there is asymmetry in...
In this paper, a two-stage framework is proposed for the energy management of microgrids, which combines a hybrid Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) forecast model and the Improved Teaching–Learning-Based Optimization (...
This research paper presents an enhanced economic load dispatch (ELD) approach using the Teaching–Learning-Based Optimization (TLBO) algorithm for 10 thermal units, examining the impact of Plug-in Electric Vehicles (PEVs) in different charging...
Shelf space is a scarce and expensive resource in the retail industry because a large number of products compete for limited display space. Thus, shelf-space allocation is frequently implemented in shops to increase product sales and profits. In the...
This research paper develops a novel hybrid approach, called hybrid particle swarm optimization–teaching–learning-based optimization (hPSO-TLBO), by combining two metaheuristic algorithms to solve optimization problems. The main idea in h...
This research proposes an enhanced teaching–learning based optimization (ETLBO) algorithm to realize an efficient path planning for a mobile robot. Four strategies are introduced to accelerate the teaching–learning based optimization (TLB...
This paper introduces a hybrid algorithm that combines machine learning and modified teaching learning-based optimization (TLBO) for enhancing smart city communication and energy management. The primary objective is to optimize the modified systems,...
To overcome the limitations of the standard Student Psychology-Based Optimization (SPBO) algorithm, such as strategy homogeneity, insufficient elite-guided diversity, and inefficient evolution of low-quality individuals, this paper proposes a Hierarc...
The maintenance of carrier-based aircraft is a critical factor restricting the availability of aircraft fleets and their capacity to sortie and operate. In this study, an aeronautical maintenance and repair task scheduling problem for carrier-based a...
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...
Precast concrete components have attracted a lot of attention due to their efficient production on off-site production lines. However, in the precast component production process, unreasonable production sequence and mold layout will reduce productio...
The main objective of this paper is to compensate power factor using teaching learning based optimization (TLBO), determine the capacitor bank optimization (CBO) algorithm, and monitor a system in real-time using cloud data logging (CDL). Implemented...
This research presents an optimized control strategy for the coordinated operation of parallel grid connected photovoltaic (PV) plants and an On Load Tap Changer (OLTC) transformer. The proposed framework integrates inverter-level active and reactive...
This article handles the challenging problem of identifying the unknown parameters of solar cell three models on one hand and of photovoltaic module three models on the other hand. This challenge serves as the basis for fault detection, control, and...
This paper presents a survey of the articles published in the period 2013–2021 related to the application of the teaching–learning-based optimization (TLBO), which reproduces the dynamics that occur in a classroom with the teacher and the...
The Gorilla Troops Optimizer (GTO) is a novel Metaheuristic Algorithm that was proposed in 2021. Its design was inspired by the lifestyle characteristics of gorillas, including migration to a known position, migration to an undiscovered position, mov...
Feature selection is an effective method to reduce the number of data features, which boosts classification performance in machine learning. This paper uses the Tsallis-entropy-based feature selection to detect the significant feature. Support Vector...
To address the problems of the single evolutionary approach, decreasing diversity, inhomogeneity, and meaningfulness in the destruction process when the teaching–learning-based optimization (TLBO) algorithm solves the no-wait flow-shop-scheduli...
A key component of the design and operation of power transmission systems is the optimal power flow (OPF) problem. To solve this problem, several optimization algorithms have been developed. The primary objectives of the program are to minimize fuel...
The modeling and control of photovoltaic and other engineering systems highly depend on the accuracy of parameter identification. However, parameter extraction for photovoltaic equivalent models typically presents a high-dimensional, strongly nonline...
Convolutional neural networks (CNNs) have excelled in artificial intelligence, particularly in image-related tasks such as classification and object recognition. However, manually designing CNN architectures demands significant domain expertise and i...
The evaluation and selection of green suppliers is an important way for enterprises to maintain sustainable development and help them reduce costs and increase efficiency. This paper proposes a multi-criteria decision-making (MCDM) model in an interv...
An XYZ compliant micropositioner has been widely mentioned in precision engineering, but the displacements in the X, Y, and Z directions are often not the same. In this study, a design and optimization for a new XYZ micropositioner are developed to o...
The present work proposes a teaching–learning-based optimization (TLBO)-tuned fuzzy proportional-integral-derivative (PID) controller of two-area hydro-thermal generating units for automatic generation control (AGC). The proposed system takes i...
When using point cloud technology to measure the dimension and geometric error of aircraft landing gear components, the point cloud data obtained after scanning may have certain differences because of the sophistication and diversity of the component...
Use of manufactured sand to replace natural sand is increasing in the last several decades. This study is devoted to the assessment of using Principal Component Analysis (PCA) together with Teaching-Learning-Based Optimization (TLBO) for enhancing th...
In this work, a DC microgrid consists of a solar photovoltaic, wind power system and fuel cells as sources interlinked with the utility grid. The appropriate sizing and positioning of electric vehicle charging stations (EVCSs) and renewable energy so...
This paper presents a methodology for the size optimization of a stand-alone hybrid PV/wind/diesel/battery system while considering the following factors: total annual cost (TAC), loss of power supply probability (LPSP), and the fuel cost of the dies...
In the face of escalating environmental degradation and dwindling resources, the imperatives of prioritizing environmental protection, and conserving resources have come sharply into focus. Therefore, remanufacturing processing, as the core of remanu...
In this work, the process of dissimilar resistance spot welding (RSW) for AISI 304 and AISI 1060 steel sheets is experimentally investigated. The effects of the main process parameters such as welding current, electrode force, welding cycle, and cool...
The fabrication of components involves the deposition of multiple beads in multiple layers for wire-arc additive manufacturing (WAAM). WAAM performed using gas metal arc welding (GMAW) allows for the manufacturing of parts through multiple-bead multi...
In this paper, an energy management schedule is proposed for a renewable energy (RE)-based grid-connected electric vehicle (EV) charging system (commercial charging station). This energy management schedule was converted into an energy management alg...
The polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP) experiment has the characteristics of low-cost, rapidity, simplicity, convenience, high sensitivity and high specificity; thus, many small and medium laboratories...
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 difficulti...
In computer vision, estimating geometric relations between two different views of the same scene has great importance due to its applications in 3D reconstruction, object recognition and digitization, image registration, pose retrieval, visual tracki...
Shape memory alloy (SMA), particularly those having a nickel–titanium combination, can memorize and regain original shape after heating. The superior properties of these alloys, such as better corrosion resistance, inherent shape memory effect,...
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