Search

This paper discusses the problem of optimal placement and sizing of passive harmonic filters to mitigate harmonics in unbalanced distribution systems. The problem is formulated as a nonlinear multiobjective optimisation problem and solved using the multiobjective genetic algorithm. The performance of the proposed algorithm is tested on unbalanced IEEE 13- and 37-bus three-phase systems. The optimal solutions are obtained based on the following objective functions: 1) minimisation of total harmonic distortion in voltage, 2) minimisation of costs of filters, 3) minimisation of voltage unbalances, and 4) a simultaneous minimisation of total harmonic distortion in voltage, costs of filters, and voltage unbalances. Finally, an analysis of the influence of uncertainties of load powers and changes in system frequency and filter parameters on filter efficiency was performed.

This study aims to present a simple hybrid approach for synthesizing heat exchanger networks (HENs). The synthesis of HEN structures employs a genetic algorithm based on a modified node representation, closely resembling the stage-wise superstructure model. This modified representation allows for addressing the internal utility exchangers throughout the entire network. Despite the nonlinear nature of the model governing the continuous variables of each structure, a relatively simple linear formulation is developed to handle these variables. This novel formulation comprises a linear programming model for maximum energy recovery, a linear correction procedure (COP), and two search loops. The COP focuses on determining the optimal values of exchangers’ heat loads and stream split ratios to reach the minimum total annual cost of the network. The study investigates four extensively studied medium- to large-scale networks from the literature. Despite the simplicity of the proposed approach, a comparison of results demonstrates its potential effectiveness.

Abstract A detailed numerical simulation of Colored Self-Compacting Concrete (CSCC) was conducted in this research. Emphasis was placed on an innovative calibration methodology tailored for ten unique CSCC mix designs. Through the incorporation of multi-objective optimization, MATLAB's Genetic Algorithm (GA) was seamlessly integrated with PFC3D, a prominent Discrete Element Modeling (DEM) software package. This integration facilitates the exchange of micro-parameter values, where MATLAB’s GA optimizes these parameters, which are then input into PFC3D to simulate the behavior of CSCC mix designs. The calibration process is fully automated through a MATLAB script, complemented by a fish script in PFC, allowing for an efficient and precise calibration mechanism that automatically terminates based on predefined criteria. Central to this approach is the Uniaxial Compressive Strength (UCS) test, which forms the foundation of the calibration process. A distinguishing aspect of this study was the incorporation of pigment effects, reflecting the cohesive behavior of cementitious components, into the micro-parameters influencing the cohesion coefficient within DEM. This innovative approach ensured significant alignment between simulations and observed macro properties, as evidenced by fitness values consistently exceeding 0.94. This investigation not only expanded the understanding of CSCC dynamics but also contributed significantly to the discourse on advanced concrete simulation methodologies, underscoring the importance of multi-objective optimization in such studies.

Accurate prediction of ice resistance plays an important role in ensuring the safety of ship sailing in polar navigation in ice areas. In recent years, machine learning has been widely used in the field of ships, among which artificial neural network (ANN) is a common method. The focus of this paper is to design an ANN model for predicting the ice resistance of polar ships. According to the traditional empirical and semi-empirical formula, appropriate input characteristic parameters are selected. The radial basis function (RBF) neural network model is built based on a large number of ship model test data, and the genetic algorithm (GA) is used to optimize the model. The research shows that the radial basis function neural network model optimized by genetic algorithm (RBF-GA) based on seven characteristic parameters input has good generalization effect. Compared with the model test and full-scale test data, the average error is about 8%, which shows that the RBF-GA model has a high accuracy, and can be used as a tool for ice resistance prediction.

AbstractIn the contemporary world, thyroid disease poses a prevalent health issue, particularly affecting women’s well-being. Recognizing the significance of maternal thyroid (MT) hormones in fetal neurodevelopment during the first half of pregnancy, this study introduces the HNN-GSO model. This groundbreaking hybrid approach, utilizing the MT dataset, integrates ResNet-50 and Artificial Neural Network (ANN) within a Glow-worm Swarm Optimization (GSO) framework for optimal parameter tuning. With a comprehensive methodology involving dataset preprocessing and Genetic Algorithm (GA) for feature selection, our model leverages ResNet-50 for feature extraction and ANN for classification tasks. Implemented in Python, the HNN-GSO model outperforms existing models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), ResNet, GoogleNet, and ANN, achieving an impressive accuracy rate of 98%. This success underscores the effectiveness of our approach in complex classification tasks within machine learning (ML) and pattern recognition, specifically tailored to the Thyroid Ultrasound Images (TUI) Dataset. To provide a comprehensive understanding of performance, additional statistical measures such as precision, recall, and F1 score were considered. The HNN-GSO model consistently outperformed competitors across these metrics, showcasing its superiority in MT classification. The HNN-GSO model seamlessly combines ResNet-50's feature extraction, ANN's classification robustness, and GSO's optimization for unparalleled performance. This research offers a promising framework for advancing ML methodologies, enhancing accuracy, and efficiency in classification tasks related to MT health. [ABSTRACT FROM AUTHOR]

The accuracy of lithium battery model parameters is the key to lithium battery state estimation. The offline parameter identification method for lithium batteries requires the nonlinear fitting of the voltage rebound curve of the hybrid pulse discharge experiment. The genetic algorithm has a strong global search ability, but it is easy to fall into local solutions. The Levenberg-Marquardt algorithm has a strong local optimization ability, but the algorithm cannot converge when the prior value is unknown. Given the above problems, this paper proposes a parameter identification method based on the Genetic-Levenberg-Marquardt (GA-LM) algorithm, which takes the sum of the squared model voltage errors as the objective function, and predicts the initial value of the parameter vector through the GA, providing the LM algorithm with prior value. In the case of unknown prior values, the GA-LM algorithm can achieve high-precision nonlinear optimization. Finally, the simulation test under the conditions of constant current discharge and hybrid pulse power discharge. The mean absolute error, mean relative error, and root mean square error of the model voltage in the two working conditions are 7.23 mV, 0.20%, 9.61 mV, and 13.37 mV, 0.37%, 15.44 mV, which shows that the algorithm has high accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Economics & Administrative Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Display omitted] • Developing GA-LSTM model to predict gas production of large-scale anaerobic digesters. • Considering long HRT of anaerobic digesters in modeling procedure. • Defining three scenarios based on WWTP parameters to assess the model's performance. • Achieving high prediction accuracies (R2 > 0.84) by the proposed model for all scenarios. An artificial neural network (ANN) model called long-short term memory (LSTM), coupled with a genetic algorithm (GA) for feature selection, was used to predict biogas production of large-scale anaerobic digesters (ADs) of Tehran South Wastewater Treatment Plant (Iran), with a biogas production of approximately 30,000 Nm3/d. In order to employ the real conditions, the hydraulic retention time (HRT) of the ADs (21 days) was considered as the LSTM look-back window. To evaluate the model predictions, three different scenarios were defined. In the first scenario, the model predicted the produced biogas by using raw wastewater characteristics and reached the coefficient of determination of R2 = 0.84. The GA selected four out of eleven parameters of raw wastewater, including loads of BOD 5 , COD, TSS, and TN (kg/d), as the most informative data for the model. In the second scenario, the model predicted the produced biogas by employing the data of the thickened sludge streams entering the ADs and yielded a higher accuracy (R2 = 0.89). In this scenario, GA selected two out of six parameters of the sludge streams, including total flow rate (m3/d) and average solids content (w/w%). Finally, in the third scenario, by putting the parameters of the two previous scenarios together, the model's prediction accuracy increased slightly (R2 = 0.90). The results demonstrated that the GA-LSTM modeling technique could achieve reliable performance in predicting biogas production of large-scale ADs by including HRT in modeling procedure. It was also found that the raw wastewater characteristics severely affect AD behavior and can be successfully used as the input data of the AD models. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles (HEVs) have emerged as a trendy technology for reducing over-dependence on fossil fuels and a global concern of gas emissions across transportation networks. This research aims to design the hybridized drivetrain of a Volkswagen (VW) Jetta MK5 vehicle on the basis of its mathematical background description and a computer-aided simulation (MATLAB/Simulink/Simscape, MATLAB R2023b). The conventional car operates through a five-speed manual gearbox, and a 2.0 TDI internal combustion engine (ICE) is first assessed. A comparative study evaluates the optimal fuel economy between the conventional and the hybrid versions based on a proportional-integral-derivative (PID) controller, whose optimal set-point is predicted and computed by a genetic algorithm (GA). For realistic hybridization, this research integrated a Parker electric motor and the diesel engine of a VW Crafter hybrid vehicle from the faculty of engineering to reduce fuel consumption and optimize the system performance of the proposed car. Moreover, a VCDS measurement unit is developed to collect vehicle data based on real-world driving scenarios. The simulation results are compared with experimental data to validate the model's accuracy. The simulation results prove the effectiveness of the proposed energy management strategy (EMS), with an approximately 89.46% reduction in fuel consumption for the hybrid powertrain compared to the gas-powered traditional vehicle, and 90.05% energy efficiency is achieved. [ABSTRACT FROM AUTHOR]

Various applications of intelligent transport systems require road traffic data that can be collected from vehicles and sent over a vehicular ad hoc network (VANET). Due to rapid mobility and limited channel capacity in a VANET, where vehicles must compete to access the roadside units (RSUs) to report their data, clustering is used to create a group of vehicles to collect, aggregate, and transfer data to RSUs acting as sink nodes. Unlike prior works that mostly focus on cluster head selection for prolonging cluster lifetime or maximizing throughput, we applied the coalitional game model to create a multi-hop cluster with the largest possible coverage area for a given transmission delay time constraint to economize the number of RSUs. The coalitional game models the profit and cost of nodes as the utility, which is a weighted function of the coverage area, amount of cluster’s members, relative velocities, distances among nodes, and transmission delay toward the sink nodes. Due to the problem complexity, the genetic algorithm is developed to obtain the model solution. The simulation results reveal that the solution quickly converges within a few generations, where the most suitable structure attains the maximum summation utility from all nodes in the coalition. Additionally, the GA-based solution approach outperforms the brute-force approach in terms of the problem scale, and the coalitional game model yields higher coverage areas compared to those obtained from the non-cooperation model. [ABSTRACT FROM AUTHOR]

A mobile ad hoc network (MANET) is a cluster of wireless mobile devices that can create a temporary network without seeking the support of any central management or infrastructure. Due to such issues, wireless communications require energy consumption, frequent data transmission, and nodes' mobility. Secondly, loss of data packets occurs because of various reasons such as traffic congestion, node mobility, or unexpected losses. An efficient vehicular ad hoc network (VANET) routing protocol is proposed in this study using a genetic algorithm (GA) and evolution-based techniques while considering all the parameters. A new fitness function (F.F) to obtain the optimum route is proposed in this study by using the routes provided by the Ad hoc on-demand multipath distance vector AOMDV routing protocol. This study uses a new type of routing mechanism based on cryptography to demonstrate how to secure V2V and V2I communications from various network threats in a VANET environment. For VANET communications, the transmission message must meet the requirements for integrity, confidentiality, and non-repudiation to ensure that a trustworthy third party can identify users with a pattern of misbehavior. While ensuring optimized route selection and secured communications, a hybrid approach AOMDV-RGA and ABC is proposed. AOMDV-RGA (AOMDV Routing using Genetic Algorithm) is used for selecting optimal routes among the routes, and ABSC (Authentication Based Secured Communication) is proposed for performing secured communication between V2V and V2I. The experimental results demonstrate the proposed technique's effectiveness compared with other previously developed techniques to address the routing and security problems of VANETs. [ABSTRACT FROM AUTHOR]

Support vector machine (SVR) has been introduced into the modeling of S‐parameters in radio‐frequency (RF) power amplifiers (PA). The modeling accuracy and speed of SVR are primarily affected by the penalty parameter c and the kernel function coefficient γ. Using the traditional grid search technique to determine these two parameters is time‐consuming and labor‐intensive, and ensuring the model's accuracy is not easy. This article proposes an S‐parameters modeling method based on PSO‐GA‐SVR to improve the SVR's modeling accuracy and speed. The model mainly focuses on particle swarm optimization (PSO) and combines selection, crossover, and mutation operations in genetic algorithms (GA). The fitness values are arranged from small to large in each iteration process, and the first 1/3 are selected for crossover and mutation. Then, the resulting new particle swarms are introduced into the original particle swarm population for searching. On the one hand, PSO‐GA extends the population size and reduces the possibility of falling into local optimization. On the other hand, due to population size expansion, the number of iteration rounds is reduced, and the modeling speed is also increased. The experimental results show that compared to SVR, GA‐SVR, and PSO‐SVR, the proposed PSO‐GA‐SVR can improve the modeling accuracy by more than one magnitude or more while also increasing modeling speed by one magnitude or more. Furthermore, compared with the classical machine learning algorithms such as gradient boosting, random forest, and gcForset, the proposed PSO‐GA‐SVR improves the modeling accuracy by one order of magnitude and the modeling speed by two orders of magnitude more than gradient boosting, random forest, and improves the modeling speed by one order of magnitude more than gcForset. [ABSTRACT FROM AUTHOR]

The proposed research work analyzes the bio-inspired problem through artificial neural networks with a feed-forward approach utilized to approximate the numerical results for singular nonlinear bio-heat equation (BHE) with boundary conditions based on four different scenarios created on the variation of environmental temperature to illustrate the effects of temperature on the human dermal region. The log-sigmoid function is used to construct the fitness function, while the optimization solvers: pattern search and genetic algorithm, are then hybridized with the active set technique, interior point technique, sequential quadratic programming for accurate and reliable results of the proposed BHE with various scenarios where the convergence of the numerical results is also analyzed. Moreover, a comparison of the proposed technique is expressed through residual error that reveals the nature of the numerical results and their efficiency. Additionally, a comprehensive statistical analysis is presented for the designed technique to better illustrate the accuracy, reliability, and efficiency of the obtained results. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of optimal placement and sizing of passive harmonic filters to mitigate harmonics in unbalanced distribution systems. The problem is formulated as a nonlinear multiobjective optimisation problem and solved using the multiobjective genetic algorithm. The performance of the proposed algorithm is tested on unbalanced IEEE 13- and 37-bus three-phase systems. The optimal solutions are obtained based on the following objective functions: 1) minimisation of total harmonic distortion in voltage, 2) minimisation of costs of filters, 3) minimisation of voltage unbalances, and 4) a simultaneous minimisation of total harmonic distortion in voltage, costs of filters, and voltage unbalances. Finally, an analysis of the influence of uncertainties of load powers and changes in system frequency and filter parameters on filter efficiency was performed. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Joint spatial–spectral feature extraction process is always playing a vital role in the accurate classification of hyperspectral imagery. Such feature extraction techniques are ever demanded for hyperspectral classification. In this proposed work three dimensional DWT (3D-DWT) is used for the decomposition of the hyperspectral image and 3D gray level cooccurrence matrix (GLCM) features are extracted for obtaining the neighborhood information. A genetic Algorithm is incorporated in this work for the selection of the best features among the extracted features for yielding good classification accuracy. The proposed method is experimented on airborne visible infrared imaging sensor (AVIRIS) data of the Indian pine site and reflective optics system imaging spectrometer (ROSIS) data of the Pavia University site. The results witness the accuracy of 94.62% for the Indian pines dataset and 96.48% for University of Pavia dataset before feature selection while only 5% of the samples in each class were used for training the 3D DWT based GLCM features. After incorporating the Genetic Algorithm for selecting the best features the accuracy is increased up to 97.67% for the Indian pines dataset and 97.99% for the University of Pavia dataset respectively, for the same 5% training samples. The proposed method is compared with the other methods and found to be more efficient. [ABSTRACT FROM AUTHOR]

Reduction of noise has a considerable effect in medical image processing and computer vision analysis. Medical images are affected by noise due to low radiation exposure, physiological sources and electronic hardware noise. This affects diagnosis quality and quantitative measurements. In this paper, optical coherence tomography images are de-noised through wavelet transform, and the wavelet threshold value is further optimised using genetic algorithm (GA). The optimal levels of wavelet decomposition and threshold correction are performed through GA. The efficacy of the proposed method is verified by comparing the results with other reported wavelet- and GA-based methods in terms of Peak-Signal-to-Noise Ratio (PSNR) parameters. The quality of the resulting image is measured through structural similarity index measure (SSIM), correlation of coefficient (COC) and edge preservation index (EPI) parameters. The improvement of the proposed approach in terms of performance parameters PSNR, COC, SSIM and EPI is respectively 2.24%, 7.9%, 17.18% and 6.32% more than the existing GA-based method considering retinal OCT image. The results indicate that the suggested algorithm effectively suppresses the speckle noise of different noise variances, and the de-noised medical image is more suitable for clinical diagnosis. [ABSTRACT FROM AUTHOR]

The design of a Proportional-Integral (PID) controller for regulating the flight control system's angle of attack is the subject of this study. To determine the settings of the suggested PID controller, the Teaching-Learning Based Optimization (TLBO) technique is used in this field for the first time. To optimize the PID controller's parameters, TLBO is used to frame the design challenge as an optimization problem. By contrasting the outcomes with those of more traditional techniques like GA, PSO & ABC the superiority of the suggested strategy is made clear. In comparison to GA, PSO & ABC based PID controllers, it has been found that TLBO optimized PID controllers provide superior dynamic performance in terms of settling time, overshoot, and undershoot. TLBO soft computing techniques are used to increase a variety of performance indices, such as Mean Square Error (MSE), Integral Absolute Error (IAE), Integral Time absolute Error (ITAE), etc. In addition, the system's robustness is examined by adjusting every system parameter in steps of 25%, from -50% to +50%. Additionally, analysis shows that even with large variations in system parameters, TLBO-optimized PID controller improvements are very resilient and do not require resets. [ABSTRACT FROM AUTHOR]

Water electrolysis, powered by renewable energy, is a leading method for producing high-purity hydrogen and oxygen. Among electrolyzer systems, alkaline and polymer exchange membrane (PEM) electrolyzers are prevalent, with PEM being preferred for large-scale energy storage due to its superior energy efficiency. Therefore, the current development of PEM technology is mainly focused on costs reduction, improvement of performance and durability. However, the degradation at high current densities has not received thorough exploration thus far. In this research, machine learning algorithms were applied to experimental data from commercial PEM water electrolyzers (PEMWE) to examine the impact of various factors including operating temperature, current density, the transport/catalyst layer interface, and catalyst loading. The study focused on how temperature affects three anode catalysts (Ir-black, IrO 2 and I r 0.7 R u 0.3 O x), the role of pore diameter in the transport layer, and different anode catalyst loadings on cell potential performance. Additionally, the influence of temperature and current density on the degradation of PEMWEs, particularly on the anode side, was analyzed using machine learning techniques. In this study, the machine learning techniques employed were support vector regression (SVR) and artificial neural network (ANN). The findings indicated that SVR, when its hyper-parameters are fine-tuned using a genetic algorithm (GA), can achieve prediction accuracy comparable to an ANN with a fully connected architecture consisting of two hidden layers, each with 30 nodes. The algorithms' predictions indicated that PEMWE can be operated at conditions that enhance the performance and life time, subsequently leading to cost reduction in hydrogen ensuring widespread adoption of PEM electrolyzers for hydrogen production. • Machine learning algorithms were used for experimental data of commercial PEMWE. • Effects of transport-catalyst layer interface and catalyst loading investigated. • Temperature and current density on PEMWE degradation have been investigated. • Support vector regression (SVR) and artificial neural network (ANN) were studied. • SVR adjusting by genetic algorithm (GA) give high accuracy prediction as ANN. [ABSTRACT FROM AUTHOR]

A detailed numerical simulation of Colored Self-Compacting Concrete (CSCC) was conducted in this research. Emphasis was placed on an innovative calibration methodology tailored for ten unique CSCC mix designs. Through the incorporation of multi-objective optimization, MATLAB's Genetic Algorithm (GA) was seamlessly integrated with PFC3D, a prominent Discrete Element Modeling (DEM) software package. This integration facilitates the exchange of micro-parameter values, where MATLAB's GA optimizes these parameters, which are then input into PFC3D to simulate the behavior of CSCC mix designs. The calibration process is fully automated through a MATLAB script, complemented by a fish script in PFC, allowing for an efficient and precise calibration mechanism that automatically terminates based on predefined criteria. Central to this approach is the Uniaxial Compressive Strength (UCS) test, which forms the foundation of the calibration process. A distinguishing aspect of this study was the incorporation of pigment effects, reflecting the cohesive behavior of cementitious components, into the micro-parameters influencing the cohesion coefficient within DEM. This innovative approach ensured significant alignment between simulations and observed macro properties, as evidenced by fitness values consistently exceeding 0.94. This investigation not only expanded the understanding of CSCC dynamics but also contributed significantly to the discourse on advanced concrete simulation methodologies, underscoring the importance of multi-objective optimization in such studies. [ABSTRACT FROM AUTHOR]

Using of ultra-wideband (UWB) technology in radio transceiver systems has increased in recent years due to high-speed data transmission, low power dissipation, low cost, and low complexity. In particular, distributed amplifier (DA) is a critical component of transceiver in UWB technology. However, designing an ultra-wideband DA with high performance becomes challenging. The DA design suffers from the tight trade-offs between the amplifier parameters such as gain, noise, linearity, input/output impedance matching, and power dissipation. In this paper, a new approach for multi-objective optimization of the DA is introduced. In the proposed approach, the meta-heuristic optimization techniques are applied over the entire bandwidth of the UWB, while the most recent optimization approaches for amplifiers are performed at the center frequency and they can't achieve the proper design specifications for wideband amplifiers. The simultaneous optimization of power gain (S21), noise figure (NF), input and output return loss (S11 and S22) are conducted over the wide bandwidth using three multi-objective optimization algorithms including Multi-Objective Inclined Planes System Optimization (MOIPO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Particle Swarm Optimization (MOPSO). The obtained results demonstrate the tapered matrix DA optimized by MOIPO exhibits better performance than others. The circuit simulations are performed in 0.18 µm TSMC RF-CMOS technology. Simulation results show that the optimized tapered matrix DA by MOIPO, compared to NSGA-II and MOPSO, exhibits a good performance over the frequency band of 0.1–28 GHz with maximum S21 of 12.9 dB, NF less than 5.9 dB, S11 and S22 below than − 10 dB over the whole frequency band. The DC power dissipation is 25 mW from a 1.5 V supply. [ABSTRACT FROM AUTHOR]

Structures in proximity subjected to a substantial lateral load (e.g., wind and earthquakes) can lead to a significant hazard known as structural pounding. If not properly mitigated, such impacts can lead to local and global damage (i.e., structural failure). Mitigation approaches can include providing a suitable separation gap distance between structures, installing adequate shock absorbers, or designing the structure for the additional pounding impact loads. Wind-induced pounding of structures can be of higher risk to buildings due to large deflections developed during wind events. The current study develops various mathematical formulas to determine the suitable separation distance between structures in proximity to avoid pounding. The developed procedure relies first on wind-load evaluations using Large Eddy Simulation (LES) models. Then, the extracted wind loads from the LES are applied to finite element method models to determine the building deflections. Various building heights, wind velocities, and flexibility levels are examined to prepare a training database for developing the mathematical formulas. A genetic algorithm is utilised to correlate the required separation gap distance to the varying parameters of the tall buildings. It was found that more complex formulas can achieve better mapping to the training database. [ABSTRACT FROM AUTHOR]

The occurrence of unfavorable weather conditions and natural disasters has always led to the imposition of extensive damages and outages at the level of distribution networks; that the number and severity of these events in recent years have often been increasing. Therefore, evaluating the resilience of the network and its reversibility ability in the face of natural disasters should be among the planning priorities for the design and operation of the network. The idea of this article is presented based on constructing the Tie-lines between the damaged sections of the network and healthy of the network in the event of a possible incident to return the service to the parts of the network without power. Finding an effective method to provide an optimal scheme to electrify the damaged parts with the lowest cost is one of the challenges of this research. Therefore, the genetic algorithm based on the elitism mechanism is proposed as one of the efficient evolutionary algorithms to optimize the total cost function, including the cost of constructing Tie-lines, the cost of reliability, and the cost of resilience. The proposed method has been applied to a feeder from the test network, and its superiority is presented through comparison with other evolutionary methods used in this study, such as particle swarm optimization and shuffled frog leaping algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent decades, water scarcity has turned into a serious problem spanning many countries, now even capable of causing or inflaming ethnic and national conflicts. While our planet has very limited freshwater resources, it has huge amounts of saltwater in seas and oceans. There is a very limited number of ways that can make saltwater drinkable, the most important of them is desalination. This study aimed to provide a method for the simultaneous optimization of desalination plant location and its water distribution network based on mathematical modeling. For this purpose, the authors formulated a non-linear mathematical model with the objective of minimizing the costs of water production and transmission. A genetic algorithm was also developed for solving the proposed nonlinear model. The method was used in a case study of Sistan and Baluchestan, which is one of Iran's most water stressed provinces. The proposed genetic algorithm managed to provide an acceptable solution for this problem in 3.74 s. The best solution was found to be constructing a desalination facility with a capacity of 394,052 cubic meters per day in a single location, that is, the city of Chabahar. The water transmission lines needed for transporting water to other parts of the province and their capacities were also determined.

Angle estimation of signals received by antenna arrays is a key step in adaptive beamforming, used to detect and locate targets in space. It is desirable to reduce costs by employing a smaller number of antennas while maintaining efficiency and degrees of freedom (DOF) of the system at the same time. Co-prime antenna arrays not only circumvent the operational constraints of cost and hardware complexity, but also boost the DOF by utilizing far fewer antennas as compared to traditional uniform linear arrays. In this paper, meta-heuristic computing paradigm for estimating arrival angle of targets with co-prime arrays incorporated in monostatic radar is proposed. The system's autocorrelation and Mean Square Error (MSE) are used for fitness function optimized by global/local search with Genetic Algorithm (GA) and Pattern Search (PS). Monte Carlo simulations are performed to evaluate the performance via estimation accuracy, MSE, and computational cost for varying numbers of impinging waves.

The Fabrication (Fab) layout design is a strategic issue and has a significant impact on the operational efficiency of semiconductor manufacturing. This research work was motivated by the actual problem to analyse and disperse the congested material flows of central corridor caused by an automated material-handling system (AMHS) in the spine-structure Fab of S Electronics in Korea, which is currently in mass production. In this paper, we suggest an efficient Fab facility layout determination method using genetic algorithm, while considering the interrelationship between manufacturing processors and AMHS. Specifically, we devise a special fitness function employing traffic congestion penalty for reverse and cross-material flows in addition to the usual material-handling distance. By using numerical experiments, we show the superiority of the suggested approach for reducing the overall distance of congested material handling by decreasing the reverse and cross-flows, which cause traffic congestions in the central corridor and entire Fab as well. We expect that this method is expected to be helpful in solving the Fab process layout problems at the Fab planning stage in the actual industrial field. [ABSTRACT FROM AUTHOR]

To mitigate the rising threat of Android malware, researchers have been actively looking for mechanisms that will enable rapid and accurate malware detection. Recently, attention has been paid to the use of audio-based features derived through the use of music information retrieval techniques. Since the exploration of these features is still in the early stages, there is a need to continue experimentation, especially with features that have yet to be used for this task. In this paper, we present the results of an ongoing investigation into the use of audio-based features for Android malware detection. In addition to extracting new audio-based features, this research aims to find the most discriminative subset of audio-based features through a comparative evaluation of Wrapper-based metaheuristic optimization algorithms on two separate datasets. First, we sonified the Android APK datasets, then extracted 191 static audio-based features from the resultant audio datasets. Fourteen different nature-inspired Wrapper-based metaheuristic optimization algorithms were evaluated for feature selection, and the selected features were then used to train the light gradient-boosting machine (LGBM) classification model. Experimental results demonstrate that the proposed approach exhibits high discriminative capabilities that can outperform other state-of-the-art techniques. The best outcome for Android malware detection was obtained using features selected by the Genetic Algorithm, which achieved 50.26% feature reduction and an improved classification accuracy of 99.72%.

Spaceborne synthetic aperture radar (SAR) is gradually being applied to hurricane observation because of its all-weather, high-resolution observation capability. In particular, the retrieval of rain rate using SAR images holds significant scientific and practical importance. However, accurately retrieving rain rate over the sea surface, particularly for high rain rate events under hurricane conditions, remains a significant challenge. The study proposes a new method for rain rate retrieval from hurricane SAR images. We have developed a cascaded feedforward neural network model based on Sentinel-1’s double-polarized C-band SAR images of 46 hurricanes to retrieve rain rate under hurricane conditions. In order to overcome the problem of local optimal solution of neural network, the genetic algorithm is employed for optimized model parameter selection. Preliminary results indicate that this approach not only enhances the neural network's iteration speed but also improves its prediction accuracy. Compared with the rain rate of the Stepped-Frequency microwave radiometers, the root mean squared error of retrieved rain rate is 3.05 mm/h and the correlation coefficient is 0.88. Furthermore, we independently verify the rain rate during Hurricane Douglas and compared with global precipitation mission 2-level dual-frequency precipitation radar rain rate product, the results demonstrate that our model can effectively retrieve rain rate in the range of 0–60 mm/h under hurricane conditions. The encouraging results prove the feasibility of the method in SAR rain rate retrieval.

Efficient planning for construction site layout is pivotal for the successful execution of a project, contributing to enhanced productivity and safety on the site. This involves identifying temporary structures or facilities required to support construction activities, choosing their size and arrangement, and strategic placing within the available space on the site. The problem of site layout planning is a challenging issue in combinatorial optimization, especially as it involves multiple objectives. Its complexity escalates with the increasing number of facilities and constraints. While existing research has proposed various analytical, heuristic, and meta-heuristic approaches to address this problem, many prior studies focused on a limited number of facilities, emphasizing the minimization of travelling distances while neglecting other pertinent cost-related and decision-making factors. This study aims to create practical and effective solutions for site layout by employing a realistic representation that takes into account not just travelling distance but also considers cost and safety relationships. A model for optimization with two objective functions has been developed to minimize travelling distance between facilities in order to minimize cost functions derived from various factors such as construction costs associated with different facility locations and transportation costs between locations, as well as to minimize risks based on the quantitative flow matrix and distance between facilities, as increasing in the frequency of interaction flow between facilities results in a higher probability of collision. In this research, a genetic algorithm (GA) is used as a heuristic optimization approach. A case study was applied to the model to highlight the benefits of the suggested approach, illustrating its effectiveness and comprehensive solutions for construction site layout planning.

With the growth of IoT, a vast number of devices are connected to the web. Consequently, both users and devices are susceptible to deception by intruders through malicious links leading to the disclosure of personal information. Hence, it is essential to identify suspicious URLs before accessing them. While numerous researchers have proposed several URL detection approaches, the machine learning-based technique stands out as particularly effective because of its ability to detect zero-day attacks; however, its success depends on the type and dimension of features utilized. In earlier research, only the lexical features of URLs were employed for classification to attain high detection speeds. However, this approach does not allow for the retrieval of comprehensive information about a website. Hence, to enhance the security of IoT devices, both lexical and page content-based features of URLs must be considered. To improve the performance of the model, the researchers extract informative features using different Feature Selection Techniques (FSTs), including filter and wrapper methods. However, challenges such as the demand for more resources, time, and handling of high-dimensional datasets encountered by individual FSTs have driven the development of hybrid FSTs. Nevertheless, the combination of a filter-based FST and a wrapper search-based Genetic Algorithm (GA) is used in the identification of malicious URLs as well as the detection of malicious links in the IoT devices research studies. Therefore, the proposed approach leverages the advantages of a variety of features and explores a hybrid FST to produce the optimal feature subset to evaluate the boosting estimators with specific hyperparameter configurations. Our proposed approach effectively fills the research gap associated with previous methodologies research 99% while keeping the computational costs minimal, making it suitable for resource-constrained devices in detecting malignant URLs.

In regions undergoing rapid urbanization, such as West Africa, land use planning (LUP) is vital to accommodate a growing population and manage natural resources. Suitability analysis modeling is a widely-used tool in LUP to determine the extent to which a land area is suitable for a designated purpose, but there is a gap in the integration of remote sensing time series data into land use decisions. The goal of this study was to incorporate remote sensing time series information with suitability analyses to inform LUP decisions in urban areas. In the study area of Kumasi, Ghana, land cover trends, and land surface temperature from 2000 to 2019 were used to understand climate change trends. Suitability analyses determined the fitness of land areas for predetermined uses. These background processes informed a genetic algorithm to project plausible futures for three land use scenarios. One scenario represented current LUP practices for addressing population growth, another scenario prioritized minimizing climate change impacts while also accommodating population growth, and the final scenario focused on both of these climate and population goals in addition to high density urban development. Each of these scenarios was successful in achieving population accommodation and respective climate change mitigation goals. The results for these scenarios provide insight into plausible land use distributions in 2050 based on different planning approaches. The genetic algorithm was able to effectively develop results for each scenario through the integration of remotely sensed trends and suitability models, providing a novel approach to land use decision-making.

Beam–slab structures account for 50–65% of a building’s total dead load and contribute to 20% of the overall cost and CO2 emissions. Despite their importance, conventional beam–slab structural optimization methods often lack search efficiency and accuracy, making them less effective for practical engineering applications. Such limitations arise from the optimization problem involving a complex solution space, particularly when considering components’ arrangement, dimensions, and load transfer paths simultaneously. To address the research gap, this study proposes a novel two-stage genetic algorithm, optimizing beam–slab layout in the first stage and component topological relationships and dimensions in the second stage. Numerical experiments on the prototype case indicate that the algorithm can generate results that meet engineering accuracy requirements within 100 iterations, outperforming comparable algorithms in both efficiency and accuracy. Additionally, this heuristic approach stands out for its independence from prior dataset training and its minimal parameter adjustment requirement, making it highly accessible to engineers without programming expertise. Statistical analysis of the algorithm’s optimization process and case studies demonstrate its robustness and adaptability to various beam–slab structural optimization problems, revealing its significant potential for practical engineering scenarios.

With the rapid development of renewable energy sources such as wind and solar, the net load characteristics of power systems have undergone fundamental changes. This paper defines quantitative analysis indicators for net load characteristics and examines how these characteristics evolve as the proportion of wind and solar energy increases. By identifying inflection points in the system’s adjustment capabilities, we categorize power systems into low, medium, and high renewable energy penetration. We then establish adjustment models that incorporate traditional coal power, hydropower, natural gas generation, adjustable loads, system interconnections, pumped-storage hydroelectricity, and new energy storage technologies. A genetic algorithm is employed to optimize and balance the net load curves under varying renewable energy proportions, analyzing the mechanism behind net load balance. A case study, based on real operational data from 2023 for a provincial power grid in western China, which is rich in renewable resources, conducts a quantitative analysis of the system’s adjustment capability inflection point and net load balancing strategies. The results demonstrate that the proposed method effectively captures the evolution of the system’s net load and reveals the mechanisms of net load balancing under different renewable energy penetration levels.