Search Results (2,530)

This paper aims to solve the multi-objective operating planning problem in the radioactive environment. First, a more complicated radiation dose model is constructed, considering difficulty levels at each operating point. Based on this model, the multi-objective operating planning problem is converted to a variant traveling salesman problem (VTSP). Second, with respect to this issue, a novel combinatorial algorithm framework, namely hyper-parameter adaptive genetic algorithm (HPAGA), integrating bio-inspired optimization with reinforcement learning, is proposed, which allows for adaptive adjustment of the hyperparameters of GA so as to obtain optimal solutions efficiently. Third, comparative studies demonstrate the superior performance of the proposed HPAGA against classical evolutionary algorithms for various TSP instances. Additionally, a case study in the simulated radioactive environment implies the potential application of HPAGA in the future. Full article

The morphology size of laser cladding is a crucial parameter that significantly impacts the quality and performance of the cladding layer. This study proposes a predictive model for the cladding morphology size based on the Least Squares Support Vector Regression (LSSVR) and the Crowned Porcupine Optimization (CPO) algorithm. Specifically, the proposed model takes three key parameters as inputs: laser power, scanning speed, and powder feeding rate, with the width and height of the cladding layer as outputs. To further enhance the predictive accuracy of the LSSVR model, a CPO-based optimization strategy is applied to adjust the penalty factor and kernel parameters. Consequently, the CPO-LSSVR model is established and evaluated against the LSSVR model and the Genetic Algorithm-optimized Backpropagation Neural Network (GA-BP) model in terms of relative error metrics. The experimental results demonstrate that the CPO-LSSVR model can achieve a significantly improved relative error of no more than 2.5%, indicating a substantial enhancement in predictive accuracy compared to other methods and showcasing its superior predictive performance. The high accuracy of the CPO-LSSVR model can effectively guide the selection of laser cladding process parameters and thereby enhance the quality and efficiency of the cladding process. Full article

This paper presents a unique approach to generate a number of cutting knowledge blocks for the surface roughness analysis of the drilling process for carbon-fiber-reinforced polymer composite (CFRP) materials. The influence of drilling on the surface quality of woven CFRP materials was investigated experimentally. The CFRP material (0/90° fiber orientation) was drilled at different cutting parameters and the surface roughness of the hole was measured. A set of tests was carried out using carbide drills of 8 mm in diameter at 50, 70, and 90 m/min cutting speeds, 2, 3, and 4 flute numbers, and 0.2, 0.3, and 0.4 mm/rev feed rates. The Simulated Annealing (SA) and Genetic Algorithm (GA) methods were used for optimization. Based on the experimental findings and optimization techniques applied, optimal cutting parameters were derived, which were subsequently adjusted to enhance surface quality. Overall, the cutting parameters are carefully optimized to achieve good surface roughness quality in the drilling of CFRP. Full article

Memristor crossbars offer promising low-power and parallel processing capabilities, making them efficient for implementing convolutional neural networks (CNNs) in terms of delay time, area, etc. However, mapping large CNN models like ResNet-18, ResNet-34, VGG-Net, etc., onto memristor crossbars is challenging due to the line resistance problem limiting crossbar size. This necessitates partitioning full-image convolution into sub-image convolution. To do so, an optimized mapping of memristor crossbars should be considered to divide full-image convolution into multiple crossbars. With limited crossbar resources, especially in edge devices, it is crucial to optimize the crossbar allocation per layer to minimize the hardware resource in term of crossbar area, delay time, and area–delay product. This paper explores three optimization scenarios: (1) optimizing total delay time under a crossbar’s area constraint, (2) optimizing total crossbar area with a crossbar’s delay time constraint, and (3) optimizing a crossbar’s area–delay-time product without constraints. The Lagrange multiplier method is employed for the constrained cases 1 and 2. For the unconstrained case 3, a genetic algorithm (GA) is used to optimize the area–delay-time product. Simulation results demonstrate that the optimization can have significant improvements over the unoptimized results. When VGG-Net is simulated, the optimization can show about 20% reduction in delay time for case 1 and 22% area reduction for case 2. Case 3 highlights the benefits of optimizing the crossbar utilization ratio for minimizing the area–delay-time product. The proposed optimization strategies can substantially enhance the neural network’s performance of memristor crossbar-based processing-in-memory architectures, especially for resource-constrained edge computing platforms. Full article

The base connection is flexible, not fully pinned/fixed, implying a nonlinear moment–rotation relationship. This deviates from a linear response, where rotation is not directly proportional to the applied moment. Numerical investigations using the commercial software ABAQUS were conducted to analyze the steel base plate connections. The finite element (FE) models were verified against previous experimental results. Moreover, numerical findings of a comprehensive parametric investigation were conducted. The studied connections were examined with different configurations, including variations in the diameter, spacing, and number of the anchor bolts; the thickness of the base plate; and the applied axial force. The current study aims to use numerical results combined with the whale optimization algorithm (WOA) and classical genetic algorithm (GA) to derive a formulation for the moment–rotation (M-θr) relationship. The distinctive aspect of this formulation is that it aims to simulate the nonlinear rotational behavior exhibited by flexible base connections under combined moment and tension loads, while also considering various parameters such as bolt number/diameter and plate thickness. The findings indicate that the WOA is capable of obtaining an optimal equation for accurately simulating the M-Ɵr relationship. This underscores the ability of the WOA to effectively address the complexity of the problem and provide a reliable equation for predicting the rotational behavior of such connections. Consequently, the WOA method can be utilized to calculate the rotational stiffness at H/150, offering valuable support for engineering design processes. Full article

Effective data fragmentation is essential in enhancing the performance of distributed database management systems (DDBMS) by strategically dividing extensive databases into smaller fragments distributed across multiple nodes. This study emphasizes horizontal fragmentation and introduces an advanced machine learning algorithm, Red Fox Optimization-based Support Vector Machine (RFO-SVM), designed for optimizing the data fragmentation process. The input database undergoes meticulous pre-processing to address missing data concerns, followed by analysis through RFO-SVM. This algorithm efficiently classifies features and target labels based on class labels. The RFO algorithm optimizes critical SVM parameters, including the kernel, kernel parameter, and boundary parameter, leveraging the accuracy metric. The resulting classified data serves as fragments for the fragmentation process. To ensure precision in fragmentation, a Genetic Algorithm (GA) allocates these fragments to diverse nodes within the DDBMS, optimizing the total allocation cost as the fitness function. The proposed model, implemented in Python, significantly contributes to the efficient fragmentation and allocation of databases in distributed systems, thereby enhancing overall performance and scalability. Full article

The influence of acoustic anisotropy on ultrasonic testing reliability poses a challenge in evaluating products from additive technologies (AT). This study investigates how elasticity constants of anisotropic materials affect defect signal amplitudes in AT products. Experimental measurements on AT samples were conducted to determine elasticity constants. Using Computational Modeling and Simulation Software (CIVA), simulations explored echo signal changes across ultrasound propagation directions. The parameters $A_{13}$ (the ratio between the velocities of ultrasonic transverse waves with vertical and horizontal polarizations at a 45-degree angle to the growth direction), $A_3$ (the ratio for waves at a 90-degree angle), and $A_g$ (the modulus of the difference between $A_{13}$ and $A_3$) were derived from wave velocity relationships and used to characterize acoustic anisotropy. Comparative analysis revealed a strong correlation (0.97) between the proposed anisotropy coefficient $A_g$ and the amplitude changes. Threshold values of $A_g$ were introduced to classify anisotropic materials based on observed amplitude changes in defect echo signals. In addition, a method leveraging deep learning to predict $A_g$ based on data from other anisotropy constants through genetic algorithm (GA)-optimized neural network (NN) architectures is proposed, offering an approach that can reduce the computational costs associated with calculating such constants. Full article

The present research focuses on optimizing 5G base station deployment and visualization, addressing the escalating demands for high data rates and low latency. The study compares the effectiveness of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Grey Wolf Optimizer (GWO) in both Urban Macro (UMa) and Remote Macro (RMa) deployment scenarios that overcome the limitations of the current method of 5G deployment, which involves adopting Non-Standalone (NSA) architecture. Emphasizing population density, the optimization process eliminates redundant base stations for enhanced efficiency. Results indicate that PSO and GA strike the optimal balance between coverage and capacity, offering valuable insights for efficient network planning. The study includes a comparison of 28 GHz and 3.6 GHz carrier frequencies for UMa, highlighting their respective efficiencies. Additionally, the research proposes a 2.6 GHz carrier frequency for Remote Macro Antenna (RMa) deployment, enhancing 5G Multi-Tier Radio Access Network (RAN) planning and providing practical solutions for achieving infrastructure reduction and improved network performance in a specific geographical context. Full article

The challenges associated with conventional methods of COVID-19 detection have prompted the exploration of alternative approaches, including the analysis of lung X-ray images. This paper introduces a novel algorithm designed to identify abnormalities in X-ray images indicative of COVID-19 by combining the maximally stable extremal regions (MSER) method with metaheuristic algorithms. The MSER method is efficient and effective under various adverse conditions, utilizing symmetry as a key property to detect regions despite changes in scaling or lighting. However, calibrating the MSER method is challenging. Our approach transforms this calibration into an optimization task, employing metaheuristic algorithms such as Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Firefly (FF), and Genetic Algorithms (GA) to find the optimal parameters for MSER. By automating the calibration process through metaheuristic optimization, we overcome the primary disadvantage of the MSER method. This innovative combination enables precise detection of abnormal regions characteristic of COVID-19 without the need for extensive datasets of labeled training images, unlike deep learning methods. Our methodology was rigorously tested across multiple databases, and the detection quality was evaluated using various indices. The experimental results demonstrate the robust capability of our algorithm to support healthcare professionals in accurately detecting COVID-19, highlighting its significant potential and effectiveness as a practical and efficient alternative for medical diagnostics and precise image analysis. Full article

In this paper, we propose an innovative approach for the path planning of Uninhabited Aerial Vehicles (UAVs) that combines an advanced Genetic Algorithm (GA) for optimising missions in advance and a geometrically based obstacle avoidance algorithm (QuickNav) for avoiding obstacles along the optimised path. The proposed approach addresses the key problem of determining an optimised trajectory for UAVs that covers multiple waypoints by enabling efficient obstacle avoidance, thus improving operational safety and efficiency. The study highlights the numerous challenges for UAV path planning by focusing on the importance of both global and local path planning approaches. To find the optimal routes, the GA utilises multiple methods of selection to optimise trajectories using the Cartesian Coordinate System (CCS) data transformed from a motion capture system. The QuickNav algorithm applies linear equations and geometric methods to detect obstacles, guaranteeing the safe navigation of UAVs and preventing real-time collisions. The proposed methodology has been proven useful in reducing the total distance travelled and computing times and successfully navigating UAVs across different scenarios with varying numbers of waypoints and obstacles, as demonstrated by simulations and real-world UAV flights. This comprehensive approach provides advantageous perspectives for real-world applications in a variety of operational situations and improves UAV autonomy, safety, and efficiency. Full article

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. Full article

The need to solvate and encapsulate hydro-sensitive molecules drives noticeable trends in the applications of cyclodextrins in the pharmaceutical industry, in foods, polymers, materials, and in agricultural science. Among them, β-cyclodextrin is one of the most used for the entrapment of phenolic acid compounds to mask the bitterness of wheat bran. In this regard, there is still a need for good data and especially for a robust predictive model that assesses the bitterness masking capabilities of β-cyclodextrin for various phenolic compounds. This study uses a dataset of 20 phenolic acids docked into the β-cyclodextrin cavity to generate three different binding constants. The data from the docking study were combined with topological, topographical, and quantum-chemical features from the ligands in a machine learning-based structure–activity relationship study. Three different models for each binding constant were computed using a combination of the genetic algorithm (GA) and multiple linear regression (MLR) approaches. The developed ML/QSAR models showed a very good performance, with high predictive ability and correlation coefficients of 0.969 and 0.984 for the training and test sets, respectively. The models revealed several factors responsible for binding with cyclodextrin, showing positive contributions toward the binding affinity values, including such features as the presence of six-membered rings in the molecule, branching, electronegativity values, and polar surface area. Full article

This paper presents the detailed design, construction and tests of a protype iron-free MRI-compatible electromagnetic actuator. The originality of this proposal lies in the use of the homogeneous static magnetic field B₀, present in the MRI bore, to ensure the electromechanical energy conversion. The armature is composed of three rectangular coils in a three-phase arrangement, which makes the actuator very light-weight and compact. The operating principle is that of an AC synchronous motor with a rotating armature. In order to design the actuator, a 3D analytical electromagnetic model is developed to predict the magnetic field produced by the armature winding. Then, a 3D finite element (FE) computation is performed to validate the analytically calculated magnetic field. The developed analytical model is then inserted into an optimization routine based on Genetic Algorithms (GAs) to obtain the prototype dimensions to be realized. Finally, the prototype is constructed and tested inside an MRI research scanner. The results indicate that the reduction in the Signal-to-Noise Ratio (SNR) and the geometrical distortion are less than 5% when the actuator is powered with a current of 10 times the rated one and when it is located very close to the subject to be imaged. Full article

Several sectors, such as agriculture and renewable energy systems, rely heavily on weather variables that are characterized by intermittent patterns. Many studies use regression and deep learning methods for weather forecasting to deal with this variability. This research employs regression models to estimate missing historical data and three different time horizons, incorporating long short-term memory (LSTM) to forecast short- to medium-term weather conditions at Quinta de Santa Bárbara in the Douro region. Additionally, a genetic algorithm (GA) is used to optimize the LSTM hyperparameters. The results obtained show that the proposed optimized LSTM effectively reduced the evaluation metrics across different time horizons. The obtained results underscore the importance of accurate weather forecasting in making important decisions in various sectors. Full article

Nowadays, due to the developments in technology and the effects of the pandemic, people have largely switched to e-commerce instead of traditional face-to-face commerce. In this sector, the product variety reaches tens of thousands, which has made it difficult to manage and to make quick decisions on inventory, promotion, pricing, and logistics. Therefore, it is thought that obtaining accurate and fast forecasting for the future will provide significant benefits to such companies in every respect. This study was built on the proposal of creating a cluster-based–genetic algorithm hybrid forecasting model including genetic algorithm (GA), cluster analysis, and some forecasting models as a new approach. In this study, unlike the literature, an attempt was made to create a more successful forecasting model for many products at the same time inside of single product forecasting. The proposed CBGA model success was compared separately to both the single prediction method successes and only genetic algorithm-based hybrid model successes by using real values from a popular B2C company. As a result, it has been observed that the forecasting success of the model proposed in this study is more successful than the forecasting made using single models or only the genetic algorithm. Full article