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Feature selection is crucial for minimizing redundancy in information and addressing the limitations of traditional classification methods when dealing with large datasets and numerous features in many machine learning applications. To improve the classification, this article introduced two hybrid methods utilizing a genetic algorithm and a gray wolf algorithm with structured dispersion norms for feature selection. These techniques involved the utilization of a genetic algorithm and a gray wolf algorithm for feature selection. The features selected by these algorithms were used in the classification process by employing a two‐layer perceptron as a classifier. The novel sparse norm is employed to assess and compute classification errors in these methodologies. To assess the effectiveness of the suggested techniques, they were compared with the existing feature selection methods using various publicly accessible datasets. The results of the experiments consistently demonstrate that the proposed methods outperform other approaches. [ABSTRACT FROM AUTHOR]

In the present investigation, process parameters were optimized in order to enhance the reduction of calcium oxide (CaO) from sugarcane molasses using acid centrifugation treatment. To predict the effects of process factors on CaO reduction efficiency, a response surface approach with a central composite design was selected. The polynomial quadratic equation was used to predict CaO removal efficiency, and the analysis of variance (ANOVA) test was utilized to assess the relevance of process factors. The appropriateness of the developed model was determined by regression analysis, which yielded a higher R‐squared value of 0.99334 ± 0.01. At the optimum process parameters of 100°C temperature, 50°Bx, and 3.50 pH, the CaO clarification efficacy of 66.17 wt.% was achieved. The experimental results indicated that for acidic centrifugation treatment, the experimentally observed CaO reduction of 65.94 wt% is in close agreement with the model equation's predicted maximum CaO reduction of 66.17 wt% with a t‐test value of 0.497726. Under such conditions, 0.982 wt.% CaO sugarcane molasses was obtained, which is low when compared to the world average of 1.5% CaO content of sugarcane molasses. Furthermore, the implementation of an artificial neural network (ANN) provided a better prediction model for CaO reduction, with a substantial R‐squared value of 0.99866. However, the genetic algorithm (GA) optimization resulted in an actual CaO reduction of 66.21 wt.% with a t‐test value of 0.497726. [ABSTRACT FROM AUTHOR]

Energy harvesting technology, as an emerging energy technology, has contributed outstandingly to the application of online monitoring sensors in power systems. The accurate parameter identification of the harvesters is crucial for their power supply applications. This work aims to investigate the parameter identification of harvesters with complex equivalent circuit models, here taking a current transformer as an example. However, previous studies focused on optimizing algorithms rather than data sources. This paper demonstrates a vector impedance quantum genetic algorithm (QGA) parameter identification method to identify the amplitude and phase information of the impedance responses. By comparing the results of the proposed method with the genetic algorithm and PSO based on impedance responses and QGA based on load resistance responses, it is proved that the proposed vector impedance QGA identification method is optimal in terms of accuracy, speed, and robustness. The root mean square errors of the output voltage and the phase difference with the primary current for the proposed method are 5.884 × 10−5 V and 4.473 × 10−4 ms. Moreover, the practical applicability of this method is validated, demonstrating its effectiveness in real‐life and industrial settings. The proposed identification method in this paper changes the source data so that the sample data are small in volume and extensive in information, which enables faster and more accurate parameter identification. This study provides a new idea for parameter identification researches. [ABSTRACT FROM AUTHOR]

The management of a three‐dimensional warehouse is a key part of the upper computer monitoring and management system for three‐dimensional warehouses. Currently, there are problems such as unreasonable planning and inability to respond to real‐time demands. Therefore, further improvement is needed to optimize the management of storage locations. The main purpose of the research is to achieve better storage stability and improve storage and retrieval efficiency. First, the study constructed a multiobjective mathematical model based on the weight, frequency of use, and category of the goods. Three objective functions were constructed. Therefore, the operational efficiency of the stacker crane and the turnover rate of items were improved. Meanwhile, the overall stability of the shelves was ensured, and the management efficiency of the warehouse was improved. At the same time, the study introduced the GA‐PSO algorithm to solve the mathematical model and optimize the goods location planning. These results confirmed that the proposed algorithm had significantly lower iteration times than traditional particle swarm optimization in different warehouse sizes and types of goods. The iteration required to reach the optimal value in Situation 1 was only 80, which was 90 fewer than PSO. Meanwhile, in Situation 2, the optimization results of the proposed algorithm in four objective functions were as high as 42.94%, 26.03%, 30.72%, and 46.15%, respectively, which increased by 1.20%, 8.04%, 5.61%, and 7.38% compared to PSO. The proposed algorithm can achieve more efficient and intelligent warehouse management, improving the efficiency and accuracy of logistics operations. It is significant for logistics industry development and enterprise competitiveness enhancement. [ABSTRACT FROM AUTHOR]

Because of the low voltage level and high current in the distribution system, as well as the high ohmic resistance of the conductors, the majority of power system losses are attributed to the distribution system. As a result, it is necessary to address the issue of reducing distribution system losses. To reduce losses in distribution systems, a wide variety of methods and algorithms have been proposed and are still being developed. The allocation of capacitors in a distribution system is one of the earliest and most effective methods for reducing system losses. With the growth of distributed generations and the diversity of their productive power, the use of capacitors in distribution systems has decreased slightly. Distributed generation resource allocation has replaced this method. Conversely, the reconfiguration of distribution systems has proven to be one of the easiest and cheapest methods for reducing distribution system losses in the past half‐century. Distribution system reconfiguration has been the subject of many studies, each with different objectives. Many studies have also reconfigured the distribution system on a daily and hourly basis. A novel method for dynamic distribution network reconfiguration is presented in this study, which utilizes capacitors and distributed generation resources simultaneously. This method divides 24 h into several periods instead of considering real‐time and hourly data. By reconfiguring and allocating capacitors and distributed generation resources during these periods, the voltage profile is improved and losses are reduced. An IEEE 33 bus network is used to test the simulations. A genetic algorithm was used to reduce losses and improve the voltage profile using MATLAB software. Based on the simulation results, this model provides better results than previous approaches. [ABSTRACT FROM AUTHOR]

The duration of a residential building project will directly influence its successful implementation; hence, it is essential to estimate a reasonable timeframe. In this study, a genetic algorithm (GA) was employed to optimize and refine the weights and thresholds of a back propagation (BP) neural network, thereby creating a GA‐BP neural network model. A dataset comprising 111 instances of residential building durations was gathered, segmented into 90 training sets and 21 test sets. The model was validated and assessed through root mean square error (RMSE), correlation coefficient (R), and average error rate, demonstrating that the GA‐BP neural network model is effective in predicting the duration of residential buildings. To enhance the predictive accuracy of the GA‐BP neural network model, this research utilized an artificial bee colony (ABC)‐improved K‐means clustering algorithm to categorize 111 experimental datasets and 33 new datasets. The results indicated that the ABC‐K‐means‐GA‐BP model exhibited robust generalization capabilities and high predictive accuracy, with the fitness function showing optimal performance after 10, 15, and 35 generations, and the best validation performances recorded as 0.0019156, 0.00035905, and 0.0036914. This validates that the proposed ABC‐K‐means‐GA‐BP neural network model significantly aids in forecasting the construction period of residential buildings, which holds substantial practical value for enhancing construction efficiency. [ABSTRACT FROM AUTHOR]

Optimization challenges effectively mirror numerous complex real‐world problems. These are typically addressed using two methodologies: approximate methods and exact methods. Within the approximate category, meta‐heuristic methods are prominent. These algorithms employ systematic principles to avoid local optima or prevent their occurrence altogether. The evolution of computing technology has spurred the development of various meta‐heuristic algorithms designed for precise and expedient resolution of optimization issues. This paper conducts a comparative analysis of meta‐heuristic algorithms applied to structural optimization. It encompasses 11 distinct meta‐heuristic optimization algorithms: atomic orbital search algorithm, chaos game optimization, energy valley optimizer, firefly algorithm, genetic algorithm, gray wolf optimization, harmony search, material generation algorithm, particle swarm optimization, social network search, and whale optimization algorithm. The efficacy of these algorithms is assessed through their application to structural problems such as trusses, cantilever‐cracked beams, welded beams, tubular columns, and I‐shaped beams. Evaluation criteria include computational efficiency, accuracy, and statistical analysis based on reports among the 50 iterations. The findings confirm the effectiveness of the algorithms, with particle swarm optimization and social network search frequently delivering superior results across most problem sets. [ABSTRACT FROM AUTHOR]

The vibration signal from the planetary gearbox exhibits nonlinear and impulsive characteristics amidst strong noise, impeding the effective extraction of fault information and compromising the accuracy of fault diagnosis. To address this challenge, a fault diagnosis method rooted in feature mode decomposition (FMD) is proposed. Initially, the critical parameters (modal number n and filter length L) of FMD are optimized using an improved genetic algorithm (IGA), and the refined FMD is employed to decompose the vibration signals from the planetary gearbox. Subsequently, a convolutional neural network integrated with the support vector machine model (CNN‐SVM) is established, leveraging the convolutional neural network for feature extraction. Ultimately, SVM iteratively optimized by the particle swarm optimization (PSO) algorithm, serves as the classification technique. Simulation and experiment results demonstrate the effectiveness of this method in extracting and identifying fault information within planetary gearboxes. [ABSTRACT FROM AUTHOR]

Process discovery approaches analyze the business data to automatically uncover structured information, known as a process model. The quality of a process model is measured using quality dimensions, completeness (replay fitness), preciseness, simplicity, and generalization. Traditional process discovery algorithms usually output a single process model. A single model may not accurately capture the observed behavior and overfit the training data. We have formed the process discovery problem in a multiobjective framework that yields several candidate solutions for the end user who can pick a suitable model based on the local environmental constraints (possibly varying). We consider the Binary Differential Evolution approach in a multiobjective framework for the task of process discovery. The proposed method employs dichotomous crossover/mutation operators. The parameters are tuned using grey relational analysis combined with the Taguchi approach. We have compared the proposed approach with the well‐known single‐objective algorithms and state‐of‐the‐art multiobjective evolutionary algorithm—Nondominated Sorting Genetic Algorithm (NSGA‐II). Additional comparison via computing a weighted average of the quality dimensions is also undertaken. Results show that the proposed algorithm is computationally efficient and produces diversified candidate solutions that score high on the fitness functions. It is shown that the process models generated by the proposed approach are superior to or at least as good as those generated by the state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

A multiobjective optimization model for energy‐saving design of green buildings is established by considering the two key indicators (energy efficiency and comfort) that are important for the design of green buildings. Using the energy consumption simulation software EnergyPlus to evaluate the fitness value of individuals, a multiobjective evolutionary optimization algorithm based on decomposition is used to optimize the above model. A multiobjective evolutionary optimization algorithm and its execution method are proposed for building energy efficiency, which integrates EnergyPlus. Taking a common multiroom residential building in northern China as an example, a multiobjective optimization is carried out. The results show that compared to classic intelligent optimization design algorithms such as nondominated sorting genetic algorithm II (NSGA‐II), the proposed method reduces discomfort time by 1.29% while only increasing energy consumption by 0.61%. [ABSTRACT FROM AUTHOR]

A dedicated bus lane is designed to give priority to buses over private cars on the road. However, this approach might waste road traffic capacity when bus demand is low and private car demand is high. Thus, it is essential to utilize bus lanes more effectively. Previous work focuses on allowing private cars to use bus lanes which may cause bus delays. To strike a balance, some approaches involve mandating private cars to leave bus lanes and limiting the number of private cars entering bus lanes. However, these highly rely on transportation infrastructure and emerging technologies. Hence, we propose a simple method of a partially space‐shared bus lane, which allows private cars to merge into the bus lane at specific locations. Lane‐changing decision is modeled as a result of speed difference between adjacent lanes. Traffic flow is simulated by the LWR model in the Lagrangian coordinate system. Two scenarios are set up—dedicated bus lane and partially space‐shared bus lane—to evaluate the partially space‐shared system. The only optimization variable is the location that allows private cars to conduct lane‐changing. We use the genetic algorithms to optimize the system. Finally, simulation results show that the partially space‐shared bus lane reduces private‐car delays and total delays of the system. The buses would experience an increase of delays, inevitably. But the bus delay increase could be limited in a small range, which maintains the bus priority. In addition, a multiple lane‐changing position strategy is better than the single position one. This approach is suitable for traffic situations with low bus demand and high private car demand. Our work is expected to contribute to the design of future urban bus lanes and improve overall traffic operations. [ABSTRACT FROM AUTHOR]

The six‐degree‐of‐freedom (6‐DOF) micromotion mechanism is a key component in extreme ultraviolet (EUV) projection lithography objectives to compensate for wavefront aberration and ensure performance stability. It is required to have high precision and high reliability. A 6‐DOF mirror micromotion mechanism of an EUV lithography objective lens with Stewart configuration is proposed, and based on the RPY (Roll‐Pitch‐Yaw) angular rotation method and the second‐type Lagrange method, the kinematic and dynamic models of the mechanism are constructed to realize the decoupling of the mechanism's kinematics and the solution of the driving force. Comparative verification with finite element transient analysis shows that the response value of the dynamic model and the finite element method (FEM) differs by 4.8% in a specific posture. A hybrid optimization method, combining genetic algorithms and particle‐swarm optimization (GA‐PSO), was proposed to optimize the structural parameters of the initial design of the motion outriggers in a micromotion mechanism. The results demonstrate a 15.1% reduction in maximum driving force, significantly enhancing outrigger driving smoothness. [ABSTRACT FROM AUTHOR]

There can be two automated storage and retrieval systems (AS/RS) in the same distribution center: one for pallets and the other for cases. Full pallets or half‐full pallets are depalletized and moved to the case storage system. The number of cases depalletized daily for an item needs to be determined based on the average daily demand for that item. Too many depalletized cases ahead of time will inflate the required storage capacity of the case storage system. On the other hand, depalletizing only the necessary cases day by day might lead to too frequent material flow, and therefore more stacker cranes are necessary. Therefore, the number of depalletized pallets affects the number and utilization of stacker cranes in the pallet storage system. It also affects the needed space in the case storage system. This study determines the most efficient number of depalletized cases for three classes of items (A, B, and C), which are determined based on their daily demand. This is done using three scenarios. The total cost is minimized, considering the utilization of the two storage systems. More investigation was done for B items, which have daily demand levels of one layer of cases or more, but less than pallet size. First, analytical equations were formulated to model the system. The model was solved using a genetic algorithm (GA) with special chromosome representations. Results showed that B items can be grouped into two subgroups that are replenished based on two different material flow scenarios. Items with relatively large demand levels have scenario 3 as the optimal one. Results also showed that the model reduces both total costs and stacker crane utilization while improving system flexibility. [ABSTRACT FROM AUTHOR]

Mathematical models that represent food processing operations are characterized by the nonlinearity of their dynamic behavior with possible discrete events, the existence of several variables of interest that are usually distributed in space, and the presence of nonlinear constraints. These features require robust optimization methods to resolve these models and to identify the optimum operating conditions of the processes. Stochastic optimization methods, often referred as metaheuristics, are effective and reliable tools to perform the global and multiobjective optimization of process units and operations involved in food engineering. In this way, this paper surveys recent advances and contributions that have applied stochastic methods for solving global and multiobjective optimization problems in food engineering. The description of the most used stochastic algorithms in food engineering is provided including the application of those methods classified as random search techniques, evolutionary methods, and swarm intelligence methods. It was observed that evolutionary methods are the most applied in solving food engineering optimization problems where the genetic algorithm and differential evolution stand out. Finally, remarks on the limitations and current challenges to improving the numerical performance of stochastic optimization methods for food engineering applications are also discussed. [ABSTRACT FROM AUTHOR]

For most ultralong bus lines, low punctuality rates and vehicle bunching are two main challenges. One potential strategy to solve these two problems is to split an ultralong line into two or three shorter lines according to historical passenger flow patterns as well as other presetting objectives and constraints. Thus, in this paper, a multiobjective optimization model is first established in consideration of both passenger flow distribution and coline metro networks. Then, the optimal truncated stop is determined by estimating the model results generated by enumerating each potential bus stop derived from related metro stops. The nondominated sorting genetic algorithm II (NSGA-II) is applied to solve the model efficiently. A case study conducted on a real bus line associated with metro networks in Beijing, China, demonstrates the efficiency of the optimization strategy adoption in terms of operating cost and passenger travel time, even with a slight increase in passenger cost. Besides, the multivehicle type is also referred in the experiments to verify economic efficiency to reduce operational costs. [ABSTRACT FROM AUTHOR]

Automated guided vehicles (AGVs), so necessary in industrial environments, require precise control of trajectory tracking to make accurate stops at logistics stations, such as loading stations, or to pick up or drop off trolleys, pallets, or racks. This paper proposes a hybrid control architecture for trajectory tracking of a hybrid tricycle-differential AGV. The control strategy combines conventional proportional integral derivative (PID) control with advanced nonlinear Lyapunov control (LPC). The LPC is used for trajectory tracking while the PID is used for speed control of the robot. The stability of the controller is demonstrated for any differentiable trajectory. When a PID optimized with genetic algorithms is compared with the proposed controller for several trajectories, the LPC outperforms it in all cases. [ABSTRACT FROM AUTHOR]

Data collection and energy consumption are critical concerns in Wireless sensor networks (WSNs). To address these issues, both clustering and routing algorithms are utilized. Therefore, this paper proposes an intelligent energy-efficient data routing scheme for WSNs utilizing a mobile sink (MS) to save energy and prolong network lifetime. The proposed scheme operates in two major modes: configure and operational modes. During the configure mode, a novel clustering mechanism is applied once, and a prescheduling cluster head (CH) selection is introduced to ensure uniform energy expenditure among sensor nodes (SNs). The scheduling technique selects successive CHs for each cluster throughout the WSNs' lifetime rounds, managed at the base station (BS) to minimize SN energy consumption. In the operational mode, two main objectives are achieved: sensing and gathering data by each CH with minimal message overhead, and establishing an optimal path for the MS using the genetic algorithm. Finally, the MS uploads the gathered data to the BS. Extensive simulations are conducted to verify the efficiency of the proposed scheme in terms of stability period, network lifetime, average energy consumption, data transmission latency, message overhead, and throughput. The results demonstrate that the proposed scheme outperforms the most recent state-of-the-art methods significantly. The results are substantiated through statistical validation via hypothesis testing utilizing ANOVA, as well as post hoc analysis. [ABSTRACT FROM AUTHOR]

This paper proposed a single-piston free piston expander-linear generator (SFPE-LG) prototype applied to organic Rankine cycle systems. Two valve timing control strategies, namely, time control strategy (TCS) and position control strategy (PCS), were developed. Based on the experimental data, a back propagation neural network (BPNN) prediction model was established. The effects of structural parameters such as neural network layers, transfer function, training function, hidden layer nodes, and learning rate on the prediction accuracy of this BPNN model were discussed. The training and prediction accuracy of the BPNN model was verified using 5-fold cross-validation and Wilcoxon signed-rank test. Moreover, the BPNN model was integrated with a genetic algorithm to predict and optimize the maximum output power of the SFPE-LG. The results showed that the BPNN model used to predict the motion characteristics and output performance of the SFPE-LG exhibits strong learning ability and high prediction accuracy. Notably, the prediction accuracy of the BPNN model is significantly higher under the PCS compared to TCS. The effect of hidden layer nodes on mean square error (MSE) and correlation coefficient (R) is greater than that of the learning rate. When the number of hidden layer nodes exceeds 30, the BPNN model consistently achieves low MSE and high R. The optimization results showed that the SFPE-LG can obtain a maximum output power of 141.69 W under the TCS, when the working parameters are inlet pressure of 0.7 MPa, intake duration of 35 ms, load resistance of 67 Ω, and expansion duration of 104 ms, respectively. [ABSTRACT FROM AUTHOR]

The rapid development of mobile Internet has promoted the rapid rise of cloud computing technology. Mobile terminal devices have greatly expanded the service capacity of mobile terminals by migrating complex computing tasks to run in the cloud. However, in the process of data exchange between mobile terminals and cloud computing centers, on the one hand, it consumes the limited power of mobile terminals, and on the other hand, it results in longer communication time, which negatively affects user QoE. Mobile cloud can effectively improve user QoE by shortening the data transmission distance, reducing the power consumption, and shortening the communication time at the same time. In this paper, we utilize the property that genetic algorithm can perform global search seeking the global optimal solution and construct a dynamic task scheduling model by combining the device-cloud link. The task scheduling model based on genetic algorithm and random scheduling algorithm is compared through comparison experiments, which show that the assignment time of the task scheduling model based on genetic algorithm is shortened by 11.82% to 48.51% and the energy consumption is reduced by 22.28% to 47.52% under different load conditions. [ABSTRACT FROM AUTHOR]

Moisture content ( M C ) plays a crucial role in evaluating the quality of tea processing. However, the current automated production line for green tea heavily relies on manual methods to determine M C , which leads to low productivity and inadequate automation. Therefore, there is an urgent need for a fast, accurate, and convenient M C detection method. In this study, near-infrared spectroscopy (NIRS) data were collected from seven stages of green tea processing and preprocessed using various techniques, such as Savitzky-Golay (SG) and detrend (DT), to reduce spectral noise. Subsequently, feature variables of the preprocessed spectral data were selected using full-band principal component analysis (PCA) and competitive adaptive reweighted sampling (CARS). Afterwards, prediction models for M C of green tea were developed using partial least squares regression (PLSR) and back-propagation neural network (BPNN). To address the convergence speed and local optima issues of BPNN, the study proposes an adaptive probabilistic genetic algorithm (AGA) to optimize the initial weights and thresholds of BPNN, including single and double-hidden layers, respectively. The results demonstrate that the double-hidden SG-DT-PCA-AGA-BPNN model outperforms the single-hidden layer model, achieving a high correlation coefficient ( R P ) of 0.994 and a low root mean square error (RMSEP) of 1.01%. This study highlights the effectiveness of increasing the number of hidden layers and using AGA to optimize the initial thresholds and weights of BPNN in improving the prediction accuracy. Furthermore, it provides a new approach to implement M C detection technology in green tea processing. [ABSTRACT FROM AUTHOR]

The energy consumption of the construction industry has been increasing year by year, posing a huge challenge to China's dual carbon goals of peaking carbon emissions and achieving carbon neutrality. The Chinese construction industry has huge potential for energy conservation and emission reduction, and the government has therefore put forward requirements for constructing green buildings and formulated strict evaluation standards. The carbon emissions of the construction industry involve various stages of the entire life cycle and are closely related to the green building design standards that meet the requirements. This article sets multiple objective functions based on the two dimensions of the carbon emissions of the entire life cycle of buildings and green building evaluation and uses the NSGA-II algorithm in genetic algorithms to optimize ten indicators selected from the two objectives. Based on this, building information modeling (BIM) modeling was carried out for an office building project in Southwest China, and energy consumption analysis and evaluation were conducted based on the project's multidisciplinary model. The dialectical relationship between the carbon emissions of the entire life cycle of buildings and the green building evaluation values was discovered, and the optimized parameter combination scheme corresponding to the Pareto solution set was obtained, providing a reference for using improved genetic algorithms and BIM technology to optimize green building design. [ABSTRACT FROM AUTHOR]

To meet the personalized distribution needs of customers, comprehensively consider customer value, the urgency of customer needs, and the impact of priority distribution to the customer on the enterprise, and based on regional restrictions, put forward vehicle-drone joint distribution path optimization problem considering customer priority. First, the goal is to minimize the sum of total distribution cost and customer priority cost integrating soft time windows and constructing a path optimization model of the vehicle-drone joint distribution. Second, a two-stage hybrid algorithm is proposed for the problem model. In the first stage, the deep neural network and the grid search improved support vector machine algorithm (DNN-GSM-SVM) are used to screen and classify customer priority features, and in the second stage, adaptive large-scale neighborhood search improved genetic algorithms (ALNS-GA) are used to solve the problem of vehicle-drone joint distribution path planning problem. Finally, combined with the numerical example, the optimization scheme of the vehicle-drone joint distribution path considering priority is analyzed. Compared with the three algorithms and error analysis, the effectiveness of the model and the two-stage algorithm was verified. Compared with the results of the scheme that does not consider priority, the results show that priority can significantly improve customer satisfaction. The efficiency of the vehicle-drone joint distribution was verified by comparing the three scenarios. [ABSTRACT FROM AUTHOR]

Substation automation systems (SAS) are critical infrastructures whose design and maintenance must be optimised to guarantee a suitable performance. In order to provide a collection of solutions that balance availability and cost, this paper explores the optimisation of the design and maintenance of a section of SAS. Multiobjective evolutionary algorithms are combined with discrete event simulation while the performance of two state-of-the-art multiobjective evolutionary algorithms is studied. On the one hand, the nondominated sorting genetic algorithm II (NSGA-II), and on the other hand, the S-metric selection evolutionary multiobjective optimisation algorithm (SMS-EMOA). Such a problem is solved from 2 and 3-objective approaches by attending to the multiobjectivisation concept. The robustness of the methodology is brought to light, and benefits were observed from the multiobjectivisation approach. Decision-makers can employ this knowledge to make informed decisions based on economic and reliability criteria. [ABSTRACT FROM AUTHOR]

The item and pod storage assignment problems, two critical issues at the strategic level in robotic mobile fulfillment systems, have a strong correlation and should be studied together. Moreover, the workload balance in each picking aisle needs to be considered in the storage assignment problems to avoid robots' congestion within picking aisles. Motivated by these, the joint optimization of item and pod storage assignment problems (J-IPSAP) with picking aisles' workload balance is studied. The mixed integer programming model of the J-IPSAP with the workload balance constraint is formulated to minimize the robots' movement distance. The improved genetic algorithm (IGA) with the decentralized pod storage assignment strategy is designed to solve the J-IPSAP model. The experimental results show that the IGA can obtain high-quality solutions when compared with Gurobi and the two-stage heuristic algorithms. The robots' movement distance is smallest when the width-to-length ratio of the storage area is close to 1, and the robots' movement distance will increase with more stringent workload balance constraints. [ABSTRACT FROM AUTHOR]

Aluminium is a noncorrosive, lightweight material used to fabricate parts for the aerospace, automobile, and construction industries. Due to the low-temperature resistance, more heat is generated. At the same time, in machining, tremendous efforts are taken to keep friction and chatter to a minimum and to attain better quality and perfect output, and also more attention is required while selecting the machining process parameters. Spindle speed, rate of feed, radial and axial depth of cut, and radial rake angle of the tool are the parameters utilized to machine aluminium 6063 using the HSS tool on CNC milling to estimate spindle and worktable vibration using a prediction model. In this study, the design of the experiment of the response surface methodology approach is used to create a second-order statistical equation for experimentation with the Design-Expert v12 software. The performance characteristics are analyzed using the ANOVA method. The spindle speed achieved the lowest vibration between 2000 and 3000 rpm. Next, axial and radial depths were the most vibration-affecting parameter compared to the rate of feed and radial rake angle of the tool. To find the best feasible response, the nondominant sorting genetic algorithm II (NSGA II) approach was trained and tested using MATLAB software. Using a Pareto-optimal technique, the optimum worktable vibration ranged from 0.00284 to 0.00165 mm/s2, whereas the spindle vibration ranged from 0.02404 to 0.01336 mm/s2. The predicted values were found to be in an excellent argument when Pareto-optimal solutions are used. [ABSTRACT FROM AUTHOR]

In this paper, we present an innovative approach for the discovery of involutory maximum distance separable (MDS) matrices over finite fields F 2 q , derived from MDS self-dual codes, by employing a technique based on genetic algorithms. The significance of involutory MDS matrices lies in their unique properties, making them valuable in various applications, particularly in coding theory and cryptography. We propose a genetic algorithm-based method that efficiently searches for involutory MDS matrices, ensuring their self-duality and maximization of distances between code words. By leveraging the genetic algorithm's ability to evolve solutions over generations, our approach automates the process of identifying optimal involutory MDS matrices. Through comprehensive experiments, we demonstrate the effectiveness of our method and also unveil essential insights into automorphism groups within MDS self-dual codes. These findings hold promise for practical applications and extend the horizons of knowledge in both coding theory and cryptographic systems. [ABSTRACT FROM AUTHOR]

To cope with the deterioration of vehicle dynamic performance induced by the increased mass and vertical stiffness of selected nonpneumatic wheels (NPWs), this study proposed a new vibration damping system (NVDS) based on the dynamic vibration absorber (DVA). First, a quarter-vehicle model containing the effective vertical stiffness of the NPW is established. Then, the effect of increased wheel mass and stiffness on vehicle dynamic performance is investigated from various aspects. To improve the handling performance of the vehicle with NPWs, a DVA-based NVDS is proposed. The sensitivities of handling performance and ride comfort to parameter changes are investigated to determine the selection range of parameters. The multiobjective genetic algorithm (GA) is employed to determine the optimal parameters of NVDS. The results obtained under different road excitations indicate that the proposed NVDS can significantly enhance the handling performance and ride comfort of the vehicle. [ABSTRACT FROM AUTHOR]

Circuit design plays a pivotal role in engineering, ensuring the creation of efficient, reliable, and cost-effective electronic devices. The complexity of modern circuit design problems has led to the exploration of multi-objective optimization techniques for circuit design optimization, as traditional optimization tools fall short in handling such problems. While metaheuristic algorithms, especially genetic algorithms, have demonstrated promise, their susceptibility to premature convergence poses challenges. This paper proposes a pioneering approach, the chaotic multi-objective Runge–Kutta algorithm (CMRUN), for circuit design optimization, building upon the Runge–Kutta optimization algorithm. By infusing chaos into the core RUN structure, a refined balance between exploration and exploitation is obtained, critical for addressing complex optimization landscapes, enabling the algorithm to navigate nonlinear and nonconvex optimization challenges effectively. This approach is extended to accommodate multiple objectives, ultimately generating Pareto Fronts for the multiple circuit design goals. The performance of CMRUN is rigorously evaluated against 11 multiobjective algorithms, encompassing 15 benchmark test functions and practical circuit design scenarios. The findings of this study underscore the efficiency and real-world applicability of CMRUN, offering valuable insights for tailoring optimization algorithms to the real-world circuit design challenges. [ABSTRACT FROM AUTHOR]

A six-dimensional force sensor with a three-beam structure was designed, and its strain and stress distribution under various load conditions were analyzed based on finite element simulation technology. According to the strain distribution, the Wheatstone bridge scheme of the sensor is designed, and the output voltage and sensitivity formulas in each direction are deduced. The optimal patch location was determined according to the strain distribution on the centerline of the strain beam. Finally, the size parameters of the elastic body were optimized based on the response surface method and multiobjective genetic algorithm, which greatly improved the sensitivity of the sensor in all directions. It can provide a useful guidance for the optimal design of the six-dimensional force sensor with a three-beam structure. [ABSTRACT FROM AUTHOR]

To investigate the heterogeneity of car-following behaviors across different vehicle combinations from the perspective of driver visual characteristics, the NGSIM dataset from I-80 and US-101 highways was selected and distinct car-following segments were extracted for analysis. Firstly, all the effective vehicle trajectories were extracted and categorized into different vehicle types based on their widths, resulting in four combination types of car-following segments. Visual angle and its change rate were introduced as variables representing driver visual characteristics. Additionally, one-way analysis of variance (ANOVA) was used to compare these variables with traditional ones. The driver's visual characteristic variables were then incorporated to improve the full velocity difference (FVD) model. Genetic algorithms were employed to calibrate the model under different car-following types, revealing pronounced behavioral variations. After implementing the enhanced drivers' visual angle (DVA) model, substantial reductions in calibration and validation errors were observed, with calibration errors decreasing by 51.93% and 42.22% and validation errors decreasing by 56.61% and 45.26%. This indicates the DVA model's remarkable adaptability and stability. Lastly, through a sensitivity analysis of errors, the DVA model demonstrated greater robustness toward the improved error evaluation function. By integrating drivers' visual characteristics, this study provides in-depth insights into heterogeneous car-following behaviors, enhancing our understanding of driver behaviors and micro-traffic simulation systems. [ABSTRACT FROM AUTHOR]

Flexible job-shop scheduling problem (FJSP) is a new research hotspot in the field of production scheduling. To solve the multiobjective FJSP problem, the production of flexible job shop can run normally and quickly. This research takes into account various characteristics of FJSP problems, such as the need to ensure the continuity and stability of processing, the existence of multiple objectives in the whole process, and the constant complexity of changes. It starts with deep learning neural networks and genetic algorithms. Long short-term memory (LSTM) and convolutional neural networks (CNN) are combined in deep learning neural networks. The new improved algorithm is based on the combination of deep learning neural networks LSTM and CNN with genetic algorithm (GA), namely, CNN-LSTM-GA algorithm. Simulation results showed that the accuracy of the CNN-LSTM-GA algorithm was between 85.2% and 95.3% in the test set. In the verification set, the minimum accuracy of the CNN-LSTM-GA algorithm was 84.6%, both of which were higher than the maximum accuracy of the other two algorithms. In the FJSP simulation experiment, the AUC value of the CNN-LSTM-GA algorithm was 0.92. After 40 iterations, the F1 value of the CNN-LSTM-GA algorithm remained above 0.8, which was significantly higher than the other two algorithms. CNN-LSTM-GA is superior to the other two algorithms in terms of prediction accuracy and overall performance of FJSP. It is more suitable for solving the discrete manufacturing job scheduling problem with FJSP characteristics. This study significantly raises the utilisation rate of the assembly shop's equipment, optimises the scheduling of FJSP, and fully utilises each processing device's versatile characteristics, which are quite useful for the production processes of domestic vehicle manufacturing companies. [ABSTRACT FROM AUTHOR]

In this article, a novel 1.8-5 GHz downconversion mixer is presented. The mixer is designed and simulated using SiGe 8HP 130 nm CMOS process technology. The proposed mixer is implemented by incorporating a double-balanced configuration, active inductor, and current mirror techniques. For performance optimization of the proposed mixer, different algorithms such as the genetic algorithm (GA), inclined plane system optimization (IPO) algorithm, and particle swarm optimization (PSO) algorithm have been used. Compared to existing works, this design shows an enhanced conversion gain (CG), a third-order input intercept point (IIP3), and return loss ( S 11 ) at the expense of the noise figure (NF). Additionally, the design consumes low power and covers a small chip area compared to other state-of-the-art devices. PSO shows the most promising results when compared to other optimization algorithms' results. According to the measurement results after PSO optimization, the mixer attains a maximum CG of 25 dB, an IIP3 of 4 dBm, and a NF of 5.2 dB at 5 GHz, while consuming only 15 mW of DC power. The mixer operates at 1.2 V and covers 0.8 mm2 die area. [ABSTRACT FROM AUTHOR]

Considering that the effect of solar radiation on the super-high arch dam temperature field remains poorly studied, the calculation accuracy of dam temperature deformation is unable to be guaranteed accordingly. To address the issue, the solar radiation effect is adequately taken into consideration by proposing a practical calculation method based on the ray-tracing algorithm, the precomputation algorithm, and the ASHRAE clear sky model in this paper. With the aid of the ASHRAE clear sky model, the solar radiation received by the super-high arch dam and reservoir water is calculated. The shading effects are calculated by means of the ray-tracing algorithm, and the precomputation technology is introduced to further enhance the computational efficiency. Finally, to guarantee the authenticity of the calculation results, the dam thermodynamic parameters are inversed by employing the hybrid genetic algorithm. Based on the application in a real-life case, we concluded that around one third of the entire dam radial temperature deformation was attributable to solar radiation during continuous sunny days. The analysis results signify a critical role for taking account of the solar radiation in dam deformation calculation. Furthermore, the practicability and utilization prospect of the proposed method was verified. [ABSTRACT FROM AUTHOR]

In the modern and complex realm of networking, the pursuit of ideal QoS metrics is a fundamental objective aimed at maximizing network efficiency and user experiences. Nonetheless, the accomplishment of this task is hindered by the diversity of networks, the unpredictability of network conditions, and the rapid growth of multimedia traffic. This manuscript presents an innovative method for enhancing the QoS in SDN by combining the load-balancing capabilities of FL and genetic algorithms. The proposed solution aims to improve the dispersed aggregation of multimedia traffic by prioritizing data privacy and ensuring secure network load distribution. By using federated learning, multiple clients can collectively participate in the training process of a global model without compromising the privacy of their sensitive information. This method safeguards user privacy while facilitating the aggregation of distributed multimedia traffic. In addition, genetic algorithms are used to optimize network load balancing, thereby ensuring the efficient use of network resources and mitigating the risk of individual node overload. As a result of extensive testing, this research has demonstrated significant improvements in QoS measurements compared to traditional methods. Our proposed technique outperforms existing techniques such as RR, weighted RR, server load, LBBSRT, and dynamic server approaches in terms of CPU and memory utilization, as well as server requests across three testing servers. This novel methodology has applications in multiple industries, including telecommunications, multimedia streaming, and cloud computing. The proposed method presents an innovative strategy for addressing the optimization of QoS metrics in SDN environments, while preserving data privacy and optimizing network resource usage. [ABSTRACT FROM AUTHOR]

The necessity for an external control mechanism that optimizes daily urban trips becomes evident when considering numerous factors at play within a complex environment. This research introduces an activity-based travel personalization tool that incorporates 10 travel decision-making factors driven by the genetic algorithm. To evaluate the framework, a complex artificial scenario is created comprising six activities in a daily plan. Afterwards, the scenario is simulated for predefined user profiles, and the results of the simulation are compared based on the users' characteristics. The simulations of the scenario successfully demonstrate the appropriate utilization of activity constraints and the efficient implementation of users' spatiotemporal priorities. In comparison to the base case, significant time savings ranging from 31.2% to 70.2% are observed in the daily activity chains of the simulations. These results indicate that the magnitude of time savings in daily activity simulations depends on how users assign values to the travel decision-making parameters, reflecting the attitudinal differences among the predefined users in this study. This tool holds promise for advancing longitudinal travel behavior research, particularly in gaining a more profound understanding of travel patterns. [ABSTRACT FROM AUTHOR]

Pharmaceutical waste management is a significant concern that poses risks to human and environmental health. The ineffective management of expired and unused medications can harm individuals and communities. This study proposes a novel approach to address the issue of pharmaceutical waste management by developing a location-routing problem (LRP) model using mixed-integer linear programing (MILP) to optimize the collection and disposal of pharmaceutical waste. The proposed model aims to minimize transportation costs, construction of collection centers, disposal costs, and carbon dioxide emissions, making it a cost-effective and environmentally sustainable approach to managing pharmaceutical waste. Initially, the feasibility, validity, and efficiency of the proposed model are examined by solving the problem in the GAMS software using CPLEX solver for small-scale problems. Sensitivity analyses are conducted to ensure the accuracy, reliability, robustness, and usefulness of the mathematical model for decision-making. In view of the inherent computational complexity of the proposed model, which is classified as nondeterministic polynomial time-hard and poses considerable difficulties when exact solutions are sought for large-scale problems, the present study resorts to two metaheuristic algorithms, specifically particle swarm optimization (PSO) and genetic algorithm (GA), as a means minimizing the computational burden. The results indicate that GA outperforms PSO in terms of objective function and solution time, with an average improvement of approximately 1% and 20%, respectively. The proposed model and algorithms provide a comprehensive approach to addressing the critical issue of pharmaceutical waste management, benefiting the healthcare industry, and society as a whole. [ABSTRACT FROM AUTHOR]

With the aim of promoting environmental sustainability and enhancing transport efficiency, battery-powered connected and automated vehicles (B-CAVs) are employed to replace diesel-powered ones in horizontal transport systems (HTSs) of container terminals. The operational efficiency of an HTS can be increased by the cooperation of B-CAVs. However, it is time-consuming to charge them. Therefore, their battery management becomes a critical issue of a transport schedule. To run container terminals more economically and efficiently, this work proposes an integrated scheduling approach to B-CAVs tasks' dispatch and route planning, where battery management is taken into account. An integer programming model is constructed with the goal of minimizing the total travel distance. Then, a sustainable charging policy is designed to ensure the consistent transport capacity of an HTS. Furthermore, a congestion-free path plan-based improved genetic algorithm is presented to obtain a near-optimal plan for dispatching B-CAVs to perform transporting and charging operations. A series of experiments are carried out to verify the effectiveness and efficiency of our approach. [ABSTRACT FROM AUTHOR]

To devise an optimum and robust fuzzy logic controller for MR damper-based structures subjected to earthquake ground motions, the multiobjective reliability-based design optimization (RBDO) using the adaptive Kriging model is performed to determine the parameters of the fuzzy logic controller. The optimization problem is formulated with two objective functions, namely, the minimization of interstory drift and average control force of the concerned structure, and subjected to a probability constraint on structural dynamic responses under the effects of random structural stiffness and stochastic earthquake loadings. To reduce the computational cost of reliability assessment, a global Kriging model is constructed in an augmented space as a surrogate for computational evaluations. Subsequently, the trained metamodel combined with the nondominated sorting genetic algorithm (NSGA-II) is integrated into the framework of RBDO for solving the fuzzy logic control (FLC) optimization problem. The feasibility and effectiveness of the multiobjective RBDO in the FLC design are finally validated by conducting numerical simulations on both linear and nonlinear structures. As demonstrated in the linear case, the fuzzy logic controllers obtained from the multiobjective RBDO show more robustness than those derived from the multiobjective deterministic design optimization (DDO). In the nonlinear case, using the multiobjective DDO to prelocate a coarse safety domain can significantly reduce the number of samples for training the metamodel and facilitate the implementation of the multiobjective RBDO; in addition, the controlled structural performance with a specified fuzzy logic controller can be further improved by considering MR damper distribution optimization. [ABSTRACT FROM AUTHOR]

A rolling bearing fault diagnosis method based on the Volterra series and kernel principal component analysis (KPCA) is proposed. In the proposed method, first, the improved genetic algorithm (IGA) is used to identify the Volterra series model of the bearing in four states: normal, rolling element fault, inner ring fault, and outer ring fault. The Volterra time-domain kernel is used as the feature vector for kernel principal component analysis to classify and identify the faults. The feasibility of the fault diagnosis method of the Volterra level and kernel principal component analysis is verified by the experimental results. [ABSTRACT FROM AUTHOR]

Virtual machine (VM) replication is a critical task in any cloud computing platform to ensure the availability of the cloud service for the end user. In this task, one primary VM resides on a physical machine (PM) and one or more replicas reside on separate PMs. In cloud computing, VM placement (VMP) is a well-studied problem in terms of different goals, such as power consumption reduction. The VMP problem can be solved by using heuristics, namely, first-fit and meta-heuristics such as the genetic algorithm. Despite extensive research into the VMP problem, there are few works that consider VM replication when choosing a VMP. In this context, we proposed studying the problem of optimal VMP considering VM replication requirements. The proposed work frames the problem at hand as a multiobjective problem and adapts a nondominated sorting genetic algorithm (NSGA-III) to address the problem. VM replicas' placement should consider several dimensions such as the geographical distance between the PM hosting the primary VM and the other PMs hosting the replicas. In addition, to this end, the proposed model aims to minimize (1) power consumption, (2) performance degradation, and (3) the distance between the PMs hosting the primary VM and its replica(s). The proposed method is thoroughly tested on a variety of computing environments with various heterogeneous VMs and PMs, including compute-intensive and memory-intensive environments. The obtained results illustrate the performance disparity between the adapted NSGA-III and MOEA/D methods and other methods of comparison, including heuristic and meta-heuristic approaches, with NSGA-III outperforming other comparison methods. For instance, in memory-intensive and in heterogeneous environments, the NSGA-III method's performance was superior to the first-fit, next-fit, best-fit, PSO, and MOEA/D methods by 58%, 62%, 64%, 55%, and 31%, respectively. [ABSTRACT FROM AUTHOR]

An efficient hub-and-spoke network (HSN) can reduce operating costs and passenger delays at hubs through proper sequencing of flights and assignment of gates. Typically, batches of flights are scheduled to arrive within brief time windows. By considering aircraft sizes/loads and locations of the available gates, there is a considerable potential for reducing the total cost. During pandemic conditions, passenger transfer time and aircraft dwell time significantly increased because of outbreak controls (e.g., COVID-19 and its variant). In addition to walking time between connecting flights, there is a significant delay for the airport staff to validate passengers' proper travel documents. The objective of this study is to minimize the total cost by optimizing the flight sequencing and gate assignment and by considering the realistic transfer delay under pandemic situations. A genetic algorithm with an elite selection strategy is developed to search for the optimal solution, which significantly reduces the total cost by 25% compared with that under existing operations, and the relations between optimized solutions and various model parameters are explored. [ABSTRACT FROM AUTHOR]

Atenolol (ATN) is a slowly biodegradable antagonist β-blocker drug and remains in the environment for a long period of time. This drug has a harmful effect on the environment and human and animal bodies. In this study, using activated persulfate with ultrasound for the degradation of ATN was investigated. The effect of independent variables including pH, ATN concentration, persulfate dose, contact time, and ultrasonic power has been studied at 5 levels. Central composite design (CCD) was used for designing the experiments in Design Expert 11.0 software. The ATN concentration was measured using high-performance liquid chromatography (HPLC). Genetic algorithms (GA) and artificial neural network (ANN) were used for optimization and prediction, respectively. The results indicated that at the optimal conditions for the experiment (pH of 6.79, reaction time of 19 min, initial ATN concentration of 15.92 mg/L, US power of 109.56 W, and PS dose of 1317.88 mg/L), the highest ATN degradation efficiency was 98.9%. The ATN degradation could be represented by the pseudo-zero-order kinetics. Also, the data of ATN were well fitted with the ANN model (R2 = 0.98). The results showed that the best pH range to eliminate ATN is the near neutral range and the GA was found to be an effective tool to optimize the experimental conditions for the removal of ATN. The ultrasonic/persulfate process as a useful technique has a high potential to remove ATN from aqueous solutions. [ABSTRACT FROM AUTHOR]

The COVID-19 virus spreads quickly and is highly contagious, and the timeliness of emergency supplies is extremely demanding, so adequate and efficient emergency supplies have become a necessary prerequisite for strict prevention and control during the outbreak. However, the available research results are still relatively few, and using the brain storm optimization algorithm to solve the emergency supplies distribution problem is still blank. In order to optimize the emergency supplies distribution system in the context of COVID-19, this paper studies the open vehicle routing problem by considering the load weight constraints, demand constraints, and time window constraints. With the shortest total distribution distance as the objective function, a mathematical model of mixed integer linear programming is established, and the crossover operation of the genetic algorithm, the destruction operator and the repair operator of the large neighborhood search algorithm are introduced in the brain storm optimization algorithm to enhance the diversity of the solution while making the solution toward the optimal direction. Through the verification of two sets of examples, we found that compared with the other 13 solution algorithms, the solution capability and solution efficiency of the brain storm optimization algorithm are better than the comparison algorithms, which can significantly reduce the length of the distribution route and lower the distribution cost, and has excellent convergence and stability. Meanwhile, it can effectively improve the distribution efficiency of emergency supplies, which is of great significance to help the emergency reception and regular prevention and control during the outbreak of the COVID-19 epidemic. In addition, it has the necessary guidance for the improvement of personnel efficiency in the supply chain and logistics industry. [ABSTRACT FROM AUTHOR]

With the rapid development of e-commerce and the dramatic upsurge in direct-to-consumer deliveries, the last-mile problem has become increasingly apparent. With the distinct advantages of bringing economies of scale and providing 24/7 contactless self-service, smart parcel lockers play a critical role in solving the last-mile problem. However, due to a lack of planning, myopia expansion, and an ambiguous profit model, smart parcel locker suppliers in China have been suffering huge economic losses, restricting their further development. In the study, on the basis of an in-depth analysis of the cost elements and major revenue sources of smart parcel lockers, we propose a bilevel programming model to optimize the location of community smart parcel lockers with the upper-level model maximizing the profit of a third-party smart parcel locker supplier and the lower-level model maximizing user satisfaction. Then, a solution algorithm based on the genetic algorithm is proposed. Finally, some numerical experiments are carried out based on a medium-scale residential community in Jiading District, Shanghai. The sensitivity analyses conducted in this study reveal how the user satisfaction evaluation and the investment budget influence the expected profit. The modelling framework and numerical results can provide third-party smart parcel locker suppliers with significant theoretical support and practical guidance on planning the investment budget and optimizing the smart parcel locker locations to maximize their profit. [ABSTRACT FROM AUTHOR]

It is difficult to optimize the drilling parameters, which makes it difficult to reduce drilling costs in the process of drilling shale gas wells in the Fuling region of Chongqing, China. This paper takes the optimization combination of drilling parameters in drilling engineering as the research object and uses a saw-tooth genetic algorithm to perform the objective function. To solve the problem, the algorithm proposes a variable population size with periodic reinitialization, which follows a saw-tooth scheme with unequal amplitude and change period (saw-tooth GA), which can optimize the drilling parameters under constraints. According to the actual situation of the wells in the Fuling region, the three algorithms are compared for the vertical section's drilling parameters. Example calculations show that the algorithm can achieve the best economic benefits at different revolutions per minute and WOB to reduce drilling costs. Compared with other methods, this algorithm has the characteristics of fast convergence, is easy to understand, and is simple to implement. The proposed algorithm is tested for the drilling parameter optimization of shale gas wells and from which it becomes evident that the saw-tooth scheme enhances the overall performance of GAs. [ABSTRACT FROM AUTHOR]

The proposed research utilizes a computational approach to attain a numerical solution for the singularly perturbed delay differential equation (SPDDE) problem arising in the neuronal variability model through artificial neural networks (ANNs) with different solvers. The log-sigmoid function is used to construct the fitness function. The implementation of ANN on SPDDE problems is formulated for different solvers and trained with different weights. The optimization solvers such as the genetic algorithm (GA), sequential quadratic programming (SQP), and pattern search (PS) are hybridized with the active set technique (AST) and the interior-point technique (IPT) and is used to check the accuracy and rapid convergence of the numerical results of the SPDDE model. The numerical outcomes demonstrate that the system is easy to handle and efficient to solve with boundary conditions. Moreover, we used the mean residual error for one hundred runs for each solver to validate the accuracy of the proposed scheme. [ABSTRACT FROM AUTHOR]

Since unexpected event occurrences are inevitable, an efficient and effective rescheduling approach is critical in freight railway day-to-day operations. Represented by the minimal-delay objectives, the most commonly used efficiency-oriented approach ignores the role of train priority and poses equity problems in rescheduling. For equity, different train priority also reflects the preference in deciding train orders; i.e., the high-priority train is likely to be favorable. However, a conflict may be laid between reducing delays and emphasizing train priority. Hence, it is critical to decide the criteria in train order for freight railway, especially with heterogeneous priority in a competitive resource. To make a tradeoff between equity and efficiency, this paper makes train priority evolutionary and proposed the dynamic train priority considering delay time and static priority. We formulate an optimization model based on the rescheduling strategies such as retime, reorder, and retrack in a complex railway network containing single-track, double-track, and quadruple-track sections. An integrated two-dimension genetic algorithm (ITGA) approach is developed to reobtain an optimized timetable of sufficient quality while meeting the time requirements for real-time rescheduling. In the experiment, the effectiveness of the ITGA approach was employed in a simple case and a real-world case in the Netherlands freight railway. The result shows that there is a synergy between delay time and train priority, where the threshold to upgrade the evolutionary train priority plays an important role. The proposed approach is compared with the benchmark solution first-in-first-out (FIFO) approach in a real-world case to verify the performance and efficiency. Our work extends the rescheduling approach considering both equity and efficiency and provides auxiliary operation support for the dispatcher's operation rescheduling. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are widely recognized for their environmentally friendly attributes and superior performance. They offer considerable potential for managing energy in low-voltage distribution networks through the use of vehicle-to-grid (V2G) and grid-to-vehicle (G2V) technologies. This article provides a detailed investigation into the management of energy in distribution networks using a combination of EVs, solar photovoltaic (PV), and diesel generators (DG). The water filling algorithm (WFA) is utilized to distribute the storage of EVs in each energy zone in an optimal manner, thereby achieving load flattening, minimizing energy costs, and reducing grid reliance. A multiobjective genetic algorithm (MOGA) is employed to solve a formulated multiobjective optimization problem for load flattening and voltage regulation, with optimal power transaction (OPT) serving as the decision variable. An adaptive neuro-fuzzy inference system (ANFIS) based EV ranking technique is employed to prioritize EVs based on their ability to provide the required services and determine optimal energy distribution (OED) for different scenarios. This study investigates the impact of OED in several scenarios and examines the influence of ANFIS prioritization on overall EV power availability and cost of charging (CoC). The findings of this study are crucial for developing effective energy management strategies that minimize energy costs and reduce grid reliance. [ABSTRACT FROM AUTHOR]

As the interest in probing deep space increases, it is necessary to enhance the autonomous navigation capabilities of the spacecraft. Since traditional navigation methods rely on ground-based radiometric tracking, the vehicle has a significant communication delay resulting in no ability to handle unexpected situations on time. Image-based optical navigation allows interplanetary spacecraft to determine their orbits autonomously. This paper explores how to accurately extract optical observations from the original images to perform autonomous navigation. First, we introduce a simple and efficient idea to locate valuable contours of the celestial body based on gradient variations. Then, we establish a rough estimation with RANSAC to remove the outliers around the edges. Next, we propose a refined estimation based on the hybrid genetic algorithm to precisely estimate the navigation observations. Lastly, numerous experiments have confirmed that our method achieves outstanding accuracy and robustness. [ABSTRACT FROM AUTHOR]

An extremely high number of geographically dispersed, energy-limited sensor nodes make up wireless sensor networks. One of the critical difficulties with these networks is their network lifetime. Wirelessly charging the sensors continuously is one technique to lengthen the network's lifespan. In order to compensate for the sensor nodes' energy through a wireless medium, a mobile charger (MC) is employed in wireless sensor networks (WRSN). Designing a charging scheme that best extends the network's lifetime in such a situation is difficult. In this paper, a demand-based charging method using unmanned aerial vehicles (UAVs) is provided for wireless rechargeable sensor networks. In this regard, first, sensors are grouped according to their geographic position using the K-means clustering technique. Then, with the aid of a fuzzy logic system, these clusters are ranked in order of priority based on the parameters of the average percentage of battery life left in the sensor nodes' batteries, the number of sensors, and critical sensors that must be charged, and the distance between each cluster's center and the MC charging station. It then displays the positions of the UAV to choose the crucial sensor nodes using a routing algorithm based on the shortest and most vital path in each cluster. Notably, the gradient-based optimization (GBO) algorithm has been applied in this work for intracluster routing. A case study for a wireless rechargeable sensor network has been carried out in MATLAB to assess the performance of the suggested design. The outcomes of the simulation show that the suggested technique was successful in extending the network's lifetime. Based on the simulation results, compared to the genetic algorithm, the proposed algorithm has been able to reduce total energy consumption, total distance during the tour, and total travel delay by 26%, 17.2%, and 25.4%, respectively. [ABSTRACT FROM AUTHOR]