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Currently, tissue product producers try to meet consumers' requirements to retain their loyalty. In perforated products, such as toilet paper, these requirements involve the paper being portioned along the perforation line and not outside of it. Thus, it becomes necessary to enhance the behavior of the perforation line in perforated tissue papers. The current study aimed to verify if the perforation line for 0° (the solution found in commercial perforated products) is the best solution to maximize the perforation efficiency. A finite element (FE) simulation was used to validate the experimental data, where the deviations from the experiments were 5.2% for the case with a 4 mm perforation length and 8.8% for a perforation of 2 mm, and optimize the perforation efficiency using the genetic algorithm while considering two different cases. In the first case, the blank distance and the perforation line angle were varied, with the best configuration being achieved with a blank distance of 0.1 mm and an inclination angle of 0.56°. For the second case, the blank distance was fixed to 1.0 mm and the only variable to be optimized was the inclination angle of the perforation line. It was found that the best angle inclination was 0.67°. In both cases, it was verified that a slight inclination in the perforation line will favor partitioning and therefore the perforation efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a digital twin enhanced approach for optimizing collaborative production scheduling in multi-enterprise manufacturing systems. A multi-objective model is developed incorporating practical constraints such as limited time windows, different production capacities, and transportation considerations. To solve the model, an Improved Non-dominated Sorting Genetic Algorithm (INSGA-II) is designed with specialized operators and strategies. The digital twin simulation is enriched with Multi-objective Decision Making based on Interaction Structures (MDIS) to obtain higher-quality solutions. Experiments demonstrate that the MDIS digital twin approach reduces manufacturing lead times and costs while improving utilization and quality compared to standard methods. This research provides an effective optimization framework to leverage cloud manufacturing resources across organizations. [ABSTRACT FROM AUTHOR]

To enhance the quality and efficiency of computer-enabled generation of papers for Test for English Majors Band 8 (TEM-8), a paper generation model supported by sparrow search algorithm-genetic algorithm was studied. First, a simplified test paper generation mathematical model was set up after analyzing and studying types and characteristics of TEM-8 tasks. In the model, quantity, type, difficulty, discrimination degree, scores, exposure, and answering time of test questions were taken into consideration. To enhance the optimizing effect of the genetic algorithm for searching test questions, the traditional genetic algorithm was improved by introducing the sparrow search algorithm into the model to achieve a better crossover rate, variance rate, optimization precision, and speed of the genetic algorithm. A new sparrow search-genetic algorithm (SSA-GA) was designed, and the optimizing effect of SSA-GA was verified to be ideal through optimizing six standard test functions. Then, SSA-GA was applied to conduct experimentation with test paper generation, and comparison with traditional genetic algorithms was also made. The values of best and average fitness of SSA-GA were better than those of the traditional genetic algorithm (GA) in the paper generation. Exposure rate and success rate in TEM-8 paper generation of SSA-GA were higher than those of traditional GA in TEM-8 paper generation. Results showed that the studied SSA-GA could implement test paper generation with higher speed and better quality. [ABSTRACT FROM AUTHOR]

With the surging energy price and environmental concerns, measures to improve energy efficiency have attracted increasing concerns of the manufacture sector, especially energy-intensive manufacturing industries such as tissue paper mills. Energy-efficiency scheduling, as a novel energy-efficient method, has attracted the attention of an increasing number of researchers in recent years. Drying process is the most energy-intensive production process in tissue paper mills, which has a great energy-saving potential. This paper aims to reduce the energy costs for the tissue paper mill, consisting of processing energy cost and set-up energy cost, through integrating drying process optimization with energy-efficient scheduling. First, the energy cost model and the scheduling model were built. Then, the energy cost of the drying process of every job in a given scheduling problem was optimized using particle swarm optimization (PSO). Afterwards, the energy cost was further optimized using energy-efficiency scheduling. In addition, a hybrid non-dominated sorting genetic algorithm II (NSGA-II) was utilized to solve the energy-efficiency scheduling problem. Finally, several real scheduling problems from a real tissue paper mill were addressed using the proposed approach to demonstrate its effectiveness in energy saving. The experiment result showed that there is a great energy-saving potential in the drying process, accounting for up to 12.53% of the total energy consumption. Moreover, the maximum energy saving ratio of the proposed approach could reach 9.03%. On the whole, the proposed approach can provide a new energy-saving method for tissue paper mills or other manufacturing industries. [ABSTRACT FROM AUTHOR]

This paper focuses on question paper template generation and its use in dynamic generation of examination question paper. Question paper template generation is a constrained based optimization problem. Choosing an efficient, scientific and rational algorithm to generate a template is the key to dynamic examination question paper generation system. By using Genetic Algorithm (GA) and educational taxonomies, this paper analyses the initial population generation, does chromosome encoding, applies genetic manipulations and experimentally proves that the generated question paper templates are best suited for the dynamic examination paper generation system. This new approach outperforms traditional algorithms that randomly generate examination papers in terms of their topic coverage, learning domains and marks distribution. [ABSTRACT FROM AUTHOR]

A Genetic algorithm is a search algorithm depends on the methodology of natural selection and natural genetics. A Mobile Ad hoc network (MANET) is a type of wireless nodes (Devices) which are free to move anywhere in the network without any constraints. The nodes which are in range can communicate each other through radio waves and those who are not in range use any routing algorithm for communication. In this review paper, we focus on the problems of MANET that has been solved by applying GA for it and highlights the characteristic and challenges of MANET in the literature. More specifically, we present the summary of review papers and basic solutions that use and in the last, we present some future direction. Consequently, we concluded that modification in Fitness function (Evaluation function) according to the problem is the base of Genetic algorithm and variation in algorithm parameters can give solutions in a reasonable time. [ABSTRACT FROM AUTHOR]

An approach based on the characterisation of individual operations by water demands and sources has been utilised to study an operating integrated pulp and paper mill. The search for novel whitewater network configurations as a function of specified process constraints and objective functions was done by means of a genetic algorithm coupled with linear programming. Potential solutions are derived from an initial superstructure representation which embodies all possible source and demand connections by an evolutive process akin to natural selection and adaptation. Various objective functions were investigated. Innovative strategies have been identified and are being considered for implementation. [Copyright &y& Elsevier]

Cross project defect prediction (CPDP) is a key method for estimating defect-prone modules of software products. CPDP is a tempting approach since it provides information about predicted defects for those projects in which data are insufficient. Recent studies specifically include instructions on how to pick training data from large datasets using feature selection (FS) process which contributes the most in the end results. The classifier helps classify the picked-up dataset in specified classes in order to predict the defective and non-defective classes. The aim of our research is to select the optimal set of features from multi-class data through a search-based optimizer for CPDP. We used the explanatory research type and quantitative approach for our experimentation. We have F1 measure as our dependent variable while as independent variables we have KNN filter, ANN filter, random forest ensemble (RFE) model, genetic algorithm (GA), and classifiers as manipulative independent variables. Our experiment follows 1 factor 1 treatment (1F1T) for RQ1 whereas for RQ2, RQ3, and RQ4, there are 1 factor 2 treatments (1F2T) design. We first carried out the explanatory data analysis (EDA) to know the nature of our dataset. Then we pre-processed our data by removing and solving the issues identified. During data preprocessing, we analyze that we have multi-class data; therefore, we first rank features and select multiple feature sets using the info gain algorithm to get maximum variation in features for multi-class dataset. To remove noise, we use ANN-filter and get significant results more than 40% to 60% compared to NN filter with base paper (all, ckloc, IG). Then we applied search-based optimizer i.e., random forest ensemble (RFE) to get the best features set for a software prediction model and we get 30% to 50% significant results compared with genetic instance selection (GIS). Then we used a classifier to predict defects for CPDP. We compare the results of the classifier with base paper classifier using F1-measure and we get almost 35% more than base paper. We validate the experiment using Wilcoxon and Cohen's d test. [ABSTRACT FROM AUTHOR]

Abstract: Alarmingly low pulp prices in early 2009 left pulp and paper mills across North America desperate for any way to improve thin profit margins. One solution that continues to gain popularity among the industry is improved energy management systems for cogeneration systems, which use steam for two purposes – to provide heat for the pulping process and to generate electricity for sale to regional providers. This paper presents an energy optimization algorithm for use in a pulp and paper mill cogeneration system. The algorithm is applicable to a number of popular mill configurations, power sale contracts, and fuel purchasing scenarios. The method is also extended to address weather-dependent cooling limitations encountered by a mill cogeneration facility, in which case an iterative solution is proposed in order to maintain convexity of the optimization problem. Results are presented in the form of three case studies. [ABSTRACT FROM AUTHOR]

As the carrier of enterprise water resources management disclosure, water information disclosure is a means of expression of enterprises' environmental responsibility. First, a corporate water information disclosure quality evaluation index system and evaluation method are established, and with the help of the projection tracing method of accelerated genetic algorithm, 27 paper companies in China are selected as a sample and the disclosure quality level is analyzed empirically. Then, the analysis is carried out in terms of three changes in vertical trends, horizontal trends and changes in laws, regulations and policies, and the results show that Chinese paper and paper product enterprises have low quality of water information disclosure, weak disclosure content and low voluntary disclosure. Finally, feasible suggestions are made based on the evaluation of disclosure issues. [ABSTRACT FROM AUTHOR]

With the increased awareness of environmental protection and the establishment of the spot market of electric power, renewable energy plays a more and more important role in energy structure adjustment. But the intermittence and instability of renewable energy often result in energy waste. Micro-grid energy storage can effectively improve the utilization rate, economy and reliability of renewable energy. To ensure the economical operation of the micro-grid system and improve the load curve, a two-stage optimal dispatch model was established based on non-dominated sorting genetic algorithm (NSGA-II) and deep Q-value reinforcement learning algorithm (DQN). In the first stage, to get the appropriate battery capacity, the minimum comprehensive operation cost and the minimum capacity fading of battery in the cycle life were selected as the optimization objectives. The optimal solution was calculated by using the improved non-dominated sorting genetic algorithm (NSGA-II). In the second stage, the DQN Algorithm was used to propose a scheduling strategy. Compared with the GA method, the DQN method was able to reduce the operating cost from 0.5829% to 1.2294%. The results showed that the method was feasible and effective. [ABSTRACT FROM AUTHOR]

Abstract: A novel system based on Bernoulli Theorem of Large Number Law and the genetic algorithms was designed and realized in this paper, which had many advantages such as self-adaptive study for difficulty coefficient of item pool and intelligent test paper construction etc. At present, the system has applied in the examination of paperless computer tests of Jinlin university of finance and economics and some satisfactory results have been also obtained. [Copyright &y& Elsevier]

Abstract: The continuation education is very important for people who have left school to work to increase their competence and skills. To avoid the disadvantages of the common test paper generating methods, genetic algorithm is used to generate the test paper automatically. The concrete design process of test paper generating based on genetic algorithm is discussed in this paper, and some corresponding parameters setting have been compared and defined. The application results demonstrated that the genetic algorithm was an effective tool in the test paper generating. [Copyright &y& Elsevier]

Abstract: The study first summarizes the characteristics of various intelligent algorithms such as improved genetic algorithm, differential evolution algorithm and ant colony algorithm adopted in test paper generation, and then proposes the parallel evolution of swarm based on ideas of shared intelligent algorithm and dynamic multi branches tree algorithm, so as to improve searching speed and achieve the effect of short-time optimization. During forming optimal individuals, classified training and repeated recognition by virtue of dynamic multi branches tree can not only avoid premature appearance but also get strong convergence. In addition, when the constraints change, the existing knowledge can be inherited. Facts have shown that this algorithm has certain theoretical significance and reference value to the development of intelligent test paper generation algorithm. [Copyright &y& Elsevier]

In the very few recently published paper by Alavidoost et al. [1] , they proposed a novel fuzzy adaptive genetic algorithm for multi-objective assembly line balancing problem, in continue of their previous presented work by Alavidoost et al. [2] , as a modification on genetic algorithm for assembly line balancing with fuzzy processing times. Despite the fact that both of them are well-written, and completely discussed their contributions, this note looks forward to collate these papers together. Likewise provides the correct order of the figures in [1] matching with their corresponding caption. [ABSTRACT FROM AUTHOR]

This paper proposes the shortest path optimization algorithm for domestic and overseas e-commerce logistics based on a bilateral search method. This paper uses the logistics distribution route optimization algorithm based on the shortest path to set the collaborative parameters. Then, it builds an adaptive optimization model for the grid planning of domestic and overseas e-commerce logistics path. The route is optimized. Then, the PSO and genetic algorithm are integrated to establish the logistics path planning model of domestic and overseas e-commerce. The superiority of the proposed route optimization algorithm in domestic and overseas e-commerce logistics distribution is verified through simulation experiments. This algorithm has high spatial positioning efficiency and high transportation efficiency. [ABSTRACT FROM AUTHOR]

This paper provides a corrected formulation to the mixed integer programming model proposed by Aydogan et al. (2012) [1] . They proposed a genetic algorithm to learn fuzzy rules for a fuzzy rule-based classification system and developed a Mixed Integer Programming model (MIP) to prune the generated rules by selecting the best set of rules to maximize predictive accuracy. However, their proposed MIP formulation contains errors, which are described in this technical note. We develop corrections and improvements to the original formulation and test it with non-parametric statistical tests on the same data sets used to evaluate the original model. The statistical analysis shows that the results of the correction formulation are significantly different from the original model. [ABSTRACT FROM AUTHOR]

Network compression coding technology is a research hotspot in the field of digital steganography and image synthesis. How to improve image quality while achieving short compression time is a problem currently faced. Based on the improved wavelet neural network theory, this paper constructs a digital steganography and image synthesis model. The model first tracks the contour of the digit to be recognized, then equalizes and resamples the contour to make it translation-invariant and scaling-invariant, and then uses multi-wavelet neural network clusters to stretch the contour shell to obtain orders of magnitude multi-resolution and its average, and finally, these shell coefficients are fed into a feedforward neural network cluster to identify this handwritten digit, solving the problem of multi-resolution decomposition of contour shells while having a high sampling rate. In the simulation process, the classification model that a single pixel is a text/non-text pixel is trained on the original gray value of the gray pixel and its neighboring pixels, and the classified text pixels are connected to a text area through an adaptive MeanShift method. The experimental results show that it is feasible to use multi-wavelet features for handwritten digit recognition. The model combines the neural network and the genetic algorithm, making full use of the advantages of both, so that the new algorithm has the learning ability and robustness of the neural network. The compression ratio after compression by ordinary wavelet coding, PSNR, MSE, and compression time are 8.4, 25 dB, 210, and 7 s, respectively. The values are 11.7, 24 dB, 207, and 11 s, respectively. At the same time, the peak signal-to-noise ratio is higher and the mean square error is lower, that is, the compression quality is better, and the accuracy of digital steganography and image synthesis is effectively improved. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the methodology used and the results obtained in the determination of moisture diffusion coefficient of non-impregnated transformer insulating paper. In order to establish the diffusion coefficient, drying curves of paper samples were obtained by means of thermogravimetric experiments. The diffusion coefficient parameters were found by applying an optimization process based on genetic algorithms. The error function between measured and simulated curves was determined, and the parameters achieving the best correspondence between measured and estimated values were obtained. As a result, a new equation for the diffusion coefficient of non-impregnated insulating paper is proposed, depending on average moisture concentration, temperature and insulation thickness. The proposed coefficient was validated through experimental cases finding a good agreement between the experimental drying curves and those obtained by simulation using the diffusion coefficient. The proposed diffusion coefficient can be used for the determination of the time required to dry power transformers in factory. [Copyright &y& Elsevier]

The ship piping layout is one of the essential tasks in the detailed design stage of a ship. Traditional manual expert design has disadvantages such as low efficiency, reliance on experience, and subjective influence. Therefore, this paper systematically proposes an intelligent arrangement method for ships' single, parallel, and branch pipelines. Firstly, the traditional genetic algorithm is improved and combined with the A* algorithm to solve the intelligent arrangement problem of a single pipeline in ships. Then, the parallel pipeline and branch pipeline are split into multiple single pipelines by combining with the connection point strategy to solve the arrangement problem of parallel pipeline and branch pipeline. Finally, the optimized A*-genetic algorithm proposed in this paper is compared with the A* algorithm, particle swarm algorithm, and the labyrinth-genetic algorithm used in previous research through simulation experiments. The results show that the A*-genetic algorithm of this paper is optimal in six indexes, including length, number of elbows, energy value, fitness value, number of optimal solutions, and average number of convergence generations, in the arrangement of the single pipeline. In solving the parallel pipeline and branch pipeline arrangement problems, the all-around performance of this paper's algorithm is better than that of A*-genetic algorithm and maze–genetic algorithm, respectively. The A*-genetic algorithm of this paper considers the quality of pipeline arrangement and the solution's efficiency. It verifies the adaptability and superiority of the algorithm for the intelligent arrangement of various types of pipelines in ship pipelines. [ABSTRACT FROM AUTHOR]

Aerodynamic/stealth optimization is a key issue during the design of a stealth UAV. Balancing the weight of different incident angles of the RCS and combining stealth characteristics with aerodynamic characteristics are hotspots of aerodynamic/stealth optimization. To address this issue, this paper introduces a radar detection probability model to solve the weight balance problem of incident angles of the RCS and a penetration efficiency model to transfer the multi-object optimization into single-objective optimization. In this paper, a parameterized model of a flying-wing UAV is selected as the research object. A gradient-free optimization algorithm based on the genetic algorithm is used for maximizing efficiency. The optimization model balances the influence of the RCS mean value and RCS peak value on stealth performance. Moreover, the model achieves an optimal entire life cycle penetration efficiency coefficient by balancing aerodynamic and stealth optimization. The results show that the optimized model improves the penetration efficiency coefficient by 13.84% and increases maximum flight sorties by 1.8%. These results prove that the model has a reasonable combination of aerodynamic and stealth optimization for UAVs undertaking penetration missions. [ABSTRACT FROM AUTHOR]

This paper introduces a novel type of connection that integrates unbonded prestressed strands (UPS) and mortise-tenon in an assembly frame structure (UPS-MTF). First, the damage process and failure modes of the joints under reciprocating horizontal loads were systematically analyzed using refined numerical models. The recommended values of the design parameters of the joints were derived from the parametric analysis results. Refined numerical modeling results reveal the diagonal compression strut mechanism within the core region of the joint. The diagonal compression struts model assists in establishing the theoretical calculation formula for the skeleton curve of shear stress–strain in the core region. Second, a genetic algorithm (GA) parameter was identified for the restoring force model of the core region to determine the parameters of the hysteresis rules. Finally, a macro-simplified analytical model of the joint was created based on the restoring force model of the core region, and parameter analysis was conducted to verify the applicability of this macro-simplified analytical model. The research results prove that the damaged form of the joint proposed in this paper originates from the shear and relative slip damage between the components in the core region. The axial compression ratio significantly affects the hysteretic performance of the joints, and the upper and lower limit values were identified for the axial compression ratio of the joints. The area and initial effective stress of the UPS exert a minimal effect on the hysteretic performance of the joint. Based on the method proposed in this paper for determining the restoring force model in the core region of the joints, the hysteresis curves obtained from the macro-simplified analytical model closely match the refined numerical analysis model results. This correspondence verifies the applicability of the macro-simplified analytical model. [ABSTRACT FROM AUTHOR]

Multiple-input-multiple-output (MIMO) systems are of interest for meeting the expected demand for higher data rates and lower delays in future wireless packet data systems. In such systems, it is optimal to simultaneously transmit to multiple users compared with a single user in a single-input-single-output system. In addition, multicarrier systems are of interest to combat frequency-selective fading that is experienced over the larger bandwidth that these future broadband systems will use. The use of dirty paper coding further complicates the matter, because the order in which the users are encoded will affect the rates that they can achieve. A well-designed cross-layer scheduling algorithm must take into account the multiple dimensions of this resource-allocation problem and other quality-of-service (QoS) parameters to fully exploit the communications channel. The scheduling problem is often expressed in terms of optimizing some utility function. Unfortunately, the search space for this optimization problem is extremely large, which prohibits an optimal exhaustive search. To this end, we investigate the use of genetic algorithms to reduce the complexity of the scheduling. This paper builds upon prior work that implements scheduling via genetic algorithms in the context of single-carrier systems using zero-forcing beamforming (ZFB). In this paper, we investigate how the genetic algorithm can be adapted to account for the effect of encoding order on the scheduling and how the scheduling can be extended to a multicarrier system. In particular, we investigate the maximum throughput and proportionally fair scheduling criteria. We demonstrate that the performance of the genetic algorithm is near optimal compared with an exhaustive search at a greatly reduced computational complexity. Furthermore, in the case of a multicarrier orthogonal frequency-division multiplexing (OFDM) system, an increase in capacity is shown relative to the single-carrier case. [ABSTRACT FROM AUTHOR]

This paper studies the cooperative multi-area coverage problem with obstacles, which requires a group of robots to cover an area while avoiding collisions. This problem is very common in scenarios such as garbage removal, mine clearance, and regional information collection. The currently proposed algorithms usually have the problem of high redundancy and weak scene scalability. This paper designs a cooperative area coverage algorithm for multiple robots. First, a set of rules is proposed to divide the area to be covered into several small areas. Then, a genetic algorithm based on circular coding is designed to allocate these divided areas to several robots. Finally, the coverage path of the robot is designed using the zigzag method, so that the robot can cover the area allocated to it. Through computational experiments, it has been verified that this algorithm has efficiency advantages over state-of-the-art algorithms in certain scenarios and has scalability for different scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective optimization analysis to improve the controller tuning of three-element control loop for the best fit to both its servo and regulatory control objectives during the process operations. The existing Proportional-Integral-Derivative (PID) controller tuning for the three-element control loop is challenging because the best setting of each controller is obtained during the concurrent analysis, but all controller settings affect the control performance of other control loops and the output responses. Furthermore, this paper highlights the determination of Upper Limit (UL) and Lower Limit (LL) bounds by using the necessity criterion of Routh-Hurwitz stability analysis. The Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used as the optimization algorithms to improve the control performances. Both optimization analysis are operated by using a developed Graphical User Interface (GUI) via MATLAB software. At the same time, the optimized PID controller settings are applied to the steam boiler drum function of the LOOP-PRO simulator. Both GA and PSO outperform the manual tuning for the three-element loop. Among them, GA performs better than PSO even though both methods are capable of suggesting highly satisfactory performances. [ABSTRACT FROM AUTHOR]

With the large-scale integration of distributed generation (DG), it is difficult to realize distribution network planning and operation under specific requirements using the traditional cluster division method based on a single criterion. To reduce the complexity of frequency regulation control strategies, this paper proposes a cluster division method that synthesizes structural and functional indexes. First, the ability of DG within a cluster to provide flexibility to the system is analyzed. Then, a cluster response speed model is proposed to cope with the frequency regulation of demand flexibility on shorter time scales. Based on the above analysis, this paper proposes a distributed generation cluster (DGC) frequency regulation response speed index. The combined electrical distance based on the impedance–power reserve (I–PR) is defined by considering the power reserve of each node of the system. The I–PR is weighted to the structural indexes to improve the division. Meanwhile, in order to enhance the convergence speed of the algorithm for the division process, an adaptive genetic algorithm (GA) based on the encoding method of the weighted network adjacency matrix is used. Finally, distributed generation cluster division is performed on two systems to verify the validity of the proposed indexes in this paper. [ABSTRACT FROM AUTHOR]

The generation of test vectors is a key technique that affects the efficiency and fault detection rate of the boundary scan test. Aiming at the local optimal solution problem of the current common test vectors generation algorithm, this paper proposes a test vectors generation algorithm based on improved GA-AO* model, through which the test vectors are generated by using the idea of heuristic search and backtracking correction. In order to speed up the heuristic search, this paper designed a heuristic function with both prior and posterior parameters to describe the influence of typical faults on the failure probability index of the test vectors. At the same time, this paper used a genetic algorithm (GA) to determine the specific values of the posterior parameters iteratively. Finally, through theoretical analysis and physical verification, compared with the test vector generated by the traditional method, the test vector generated by this method is optimized on the prior failure probability index and performs better in the physical experiment. [ABSTRACT FROM AUTHOR]

Abstract: This article uses lightweight J2EE tools based on B/S architecture to design an auto-generated paper management system. This system includes several modules, namely: user management, subject management, classification management, question entry, question management, paper generation, and paper management. The design process performs the analysis and composes the examination paper using an efficient algorithm with a high possibility of success. With this algorithm, the user needs to identify the subject, the question type and the difficulty level. From this input, the examination paper will be generated automatically. On the basis of a study of the JSP and Microsoft Word interface, we find that the system combines with Word very well. Therefore the editing of questions and answers is performed using Word and the final paper may be stored as “.doc” files. The system has characteristics such as easy operation, a good interface, good usability, high security, high stability, and reliability. [Copyright &y& Elsevier]

In the recent era, Transportation considers the most powerful component of the business logistics system. Likewise, there is an interdependent relationship between the transportation and logistics systems. This paper aims to make a comparative study of logistics transportation problems based on intelligence algorithms. The researchers surveyed the previous studies conducted in the Artificial Intelligent field to solve complex problems. In this research study, the authors focused on techniques that are mostly applied in transportation and logistics systems, especially, Artificial Neural Network, Genetic Algorithm, and Fuzzy Logic models. Also, a proposed model and algorithm was done to obtain customers' and organizations' satisfaction. Artificial Neural Network uses as a decision tool that combines the system stat sets and the operation state-dependent sets. As well, the genetic algorithm combines the best parameters as a method to finds the best evaluation solutions. And fuzzy logic uses a fuzzy set to help decision-makers in making the best decisions in multiple fields. Finally, authors recommended to work in two new areas which are FGA, NFGA Algorithms to solve complex and multimodal problems that faces transportation logistics sector. [ABSTRACT FROM AUTHOR]

The agile remote sensing satellite scheduling problem (ARSSSP) for large-scale tasks needs to simultaneously address the difficulties of complex constraints and a huge solution space. Taking inspiration from the quantum genetic algorithm (QGA), a multi-adaptive strategies-based higher-order quantum genetic algorithm (MAS-HOQGA) is proposed for solving the agile remote sensing satellites scheduling problem in this paper. In order to adapt to the requirements of engineering applications, this study combines the total task number and the total task priority as the optimization goal of the scheduling scheme. Firstly, we comprehensively considered the time-dependent characteristics of agile remote sensing satellites, attitude maneuverability, energy balance, and data storage constraints and established a satellite scheduling model that integrates multiple constraints. Then, quantum register operators, adaptive evolution operations, and adaptive mutation transfer operations were introduced to ensure global optimization while reducing time consumption. Finally, this paper demonstrated, through computational experiments, that the MAS-HOQGA exhibits high computational efficiency and excellent global optimization ability in the scheduling process of agile remote sensing satellites for large-scale tasks, while effectively avoiding the problem that the traditional QGA has, namely low solution efficiency and the tendency to easily fall into local optima. This method can be considered for application to the engineering practice of agile remote sensing satellite scheduling for large-scale tasks. [ABSTRACT FROM AUTHOR]

In this paper, we develop a multi-objective integrated optimization method for feeder buses of rail transit based on realistic considerations. We propose a bus stop selection method that considers the influence of shared motorcycles, which can score the importance of alternative bus stops and select those with the highest scores as objectives. The objective of the model in this paper is to minimize both the travel costs of passengers and the operating costs of the bus company. This is achieved by optimizing feeder bus routes, the frequency of departures, and interchange discounts to enhance the connectivity between feeder buses and rail transit. In addition, to ensure the feasibility of generated routes in the real road network, a genetic algorithm encoded with priority is used to solve this model. We use the Xingyao Road subway station in Kunming as an example, and the results show that the optimization method is effective. [ABSTRACT FROM AUTHOR]

In this paper, a joint optimization algorithm of offloading decision, energy harvesting time, and unmanned aerial vehicle (UAV) location is proposed for user equipment (UEs)'s task completion latency problem in a communication–sensing–computing integration scenario with wireless energy supply. Under the constraints of causality of energy harvesting consumption by the UEs and conditional mutual information, the total latency minimization problem of the UEs is established. Firstly, the optimization variables of the problem are transformed from three variables of offloading decision, energy harvesting time, and UAV location to two variables of offloading decision and UAV location by means of the derived closed expression, and then the transformed optimization problem is decomposed into the offloading decision optimization sub-problem and the UAV location optimization sub-problem to be solved alternately and iteratively. The genetic algorithm is employed to tackle the optimization sub-problem of offloading decisions, and the successive convex approximation algorithm is applied to the drone positioning optimization sub-problem. Simulation results show that the proposed algorithm in this paper reduces the average task completion latency by 35 percent and 15 percent, respectively, compared to the two baseline algorithms for different numbers of UEs. [ABSTRACT FROM AUTHOR]

The data presented in this paper are related to the paper entitled "Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection", available in the Energies journal. Here, data are included to show the results of an Under-Frequency Load Shedding (UFLS) scheme that considers the injection of virtual inertia by a VSC-HVDC link. The data obtained in six cases which were considered and analyzed are shown. In this paper, each case represents a different frequency response configuration in the event of generation loss, taking into account the presence or absence of a VSC-HVDC link, traditional and optimized UFLS schemes, as well as the injection of virtual inertia by the VSC-HVDC link. Data for each example contain the state of the relay, threshold, position in every delay, load shed, and relay configuration parameters. Data were obtained through Digsilent Power Factory and Python simulations. The purpose of this dataset is so that other researchers can reproduce the results reported in our paper. Dataset: 10.5281/zenodo.11371324 Dataset License: Creative Commons Attribution 4.0 International (CC BY 4.0) [ABSTRACT FROM AUTHOR]

Many companies prefer to use third party logistics firms to deliver their goods and as such planning the return of the vehicles to the depot is not required. This is called open vehicle routing problem (OVRP). In literature, the OVRP is handled with minimum distance as objective function like vehicle routing problem. But in the real world, the objective function achieves minimum many costs like standard routing cost, stopping by cost and the deviation cost. The standard routes are previously defined under free market conditions by third party logistic firms. The deviation from the standard route is required to arrive cities which are not on the standard route. The stop by cost occurs on the delivery points. In this paper mentioned three costs are considered in the objective function while many papers consider only distance related costs in the literature. This paper proposes a new mathematical model for the OVRP. In the constraints, the last points of the routes are researched in detail. The standard route costs are determined by considering the last point of the route. Because of the NP-hard structure of the OVRP, the proposed mathematical model is solved with a hybrid metaheuristic called Civilized Genetic Algorithm (CGA). CGA is developed by hybridizing a modified genetic algorithm and a local search algorithm. The application of this study is implemented for the delivery routing of a combi boiler producer in Turkey. The third party logistic firms may use this proposed model and the solution approach for the real life applications. [ABSTRACT FROM AUTHOR]

Space layout design is a critical aspect of architectural design, influencing functionality and aesthetics. The inherent combinatorial nature of layout design poses challenges for traditional planning approaches; thus, it demands the exploration of novel methods. This paper presents a novel framework that leverages the potential of deep reinforcement learning (RL) algorithms to optimize space layouts. RL has demonstrated remarkable success in addressing complex decision-making problems, yet its application in the design process remains relatively unexplored. We argue that RL is particularly well-suited for the design process due to its ability to accommodate offline tasks and seamless integration with existing computer-aided design software, effectively acting as a simulator for design exploration. Framing space layout design as an RL problem and employing RL methods allows for the automated exploration of the expansive design space, thereby enhancing the discovery of innovative solutions. This paper also elucidates the synergy between the design process and the RL problem, which opens new avenues for exploring the potential of RL algorithms in design. We aim to foster experimentation and collaboration within the RL and architecture communities. To facilitate our research, we have developed SpaceLayoutGym , an environment specifically designed for space layout design tasks. SpaceLayoutGym serves as a customizable environment that encapsulates the essential elements of the layout design process within an RL framework. To showcase the effectiveness of SpaceLayoutGym and the capabilities of RL as an artificial space layout designer, we employ the Proximal Policy Optimization (PPO) algorithm to train the RL agent in selected design scenarios with both geometrical constraints and topological objectives. The study further extends to contrast the effectiveness of PPO agents with that of genetic algorithms, and also includes a comparative analysis with existing layouts. Our results demonstrate the potential of RL to optimize space layouts, offering a promising direction for the future of artificial intelligence-aided design. [ABSTRACT FROM AUTHOR]

Abstract: In a previous work, a moisture diffusion coefficient for non-impregnated transformer insulation paper was proposed, which is useful for analysing the moisture dynamics in electrical power transformer insulation during its construction stage. This diffusion coefficient was determined using a set of experimental drying curves obtained in a thermogravimetric analyzer and an optimization process that employs genetic algorithms. The expression for the moisture diffusion coefficient proposed in that previous work is dependent on the temperature, insulation thickness and average moisture concentration. The use of average moisture concentration is not common in diffusion models. In this paper, a new expression for the moisture diffusion coefficient is proposed in which the dependence on average moisture has been substituted by a dependence on the local concentration. This new expression was obtained that is more suitable for diffusion models. To obtain that expression, the same experimental data used in the previous study have been used, but the genetic algorithm’s optimization process was modified. In this paper, a description of the applied procedure is detailed, and the new proposed expression is presented. Finally, a comparison between both equations, the one dependent on average moisture and the one dependent on the local moisture, is performed using a drying diffusion model implemented in a finite element commercial software package. [Copyright &y& Elsevier]

Abstract: Availability analysis has been an important issue in the design field of any Industrial system as the system structure has become more complicated. Also, the system availability is affected by many factors such as design, manufacturing, installation, etc., and so it may be extremely difficult to model, analyze and predict the failure behavior of the system. The purpose of this paper is to develop a new approach for computing various performance measures, namely reliability, availability, MTBF (mean time between failures), ENOF (expected number of failures), failure rate and repair time, for any industrial system. In the proposed approach, the failure rates and repair times of all constituent components are obtained using genetic algorithms and then various performance measures are computed using fuzzy lambda–tau methodology. Washing system, the major part of paper industry is the subject of study. The interactions among the working components are modeled using Petri nets. Failure and repair rates are represented using triangular fuzzy numbers as they allow expert opinion, linguistic variables, operating conditions, uncertainty and imprecision in reliability information to be incorporated into system model. Based on calculated reliability parameters, a structured framework has been developed that may help the maintenance engineers to analyze and predict the system behavior. [Copyright &y& Elsevier]

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Power system operation and control are based on power flow calculations. In order to solve the uncertainty of the increasing penetration of renewable energy, the voltage fluctuation at the load point increases in the distribution network, and the inaccuracy of the power flow calculation due to the insufficient power flow data collection capability of the traditional power system. In this paper, a data‐driven power flow analysis model is proposed, a back propagation neural network combined with genetic algorithm (GA) and adaptive moment estimation (ADAM) optimization algorithm model is constructed to analyze the power flow calculation method of distribution networks under stochasticity. Firstly, the power flow initial value information, topology characteristics, and power factor index are introduced to construct a training set, and the mapping relationship between bus voltage and power is fully explored by training the regression model. Second, the GA‐ADAM algorithm is used to optimize the initial values and weight parameters of the model. Finally, it is verified based on IEEE‐33 bus distribution model, and the model is used for power flow calculation, and compared with other methods through each relevant error evaluation indicators. The results show that the model constructed in this paper has small error indicators and high accuracy, which improves the efficiency and accuracy of power flow calculation. [ABSTRACT FROM AUTHOR]

The three-phase imbalance in low-voltage distribution networks (LVDNs) seriously threatens the security and stability of the power system. At present, a standard solution is automatic phase commutation, but this method has limitations because it does not address the branch imbalance and premature convergence or instability of the commutation algorithm. Therefore, this paper proposes a novel refined regulation commutation system, combined with a modified optimized commutation algorithm, and designs a model and simulation for feasibility verification. The refined regulatory model incorporates branch control units into the traditional commutation system. This effectively disperses the main controller's functions to each branch and collaborates with intelligent fusion terminals for precise adjustment. The commutation algorithm designed in this paper, combined with the above model, adopts strategies such as symbol encoding, cubic chaotic mapping, and adaptive adjustment based on traditional genetic algorithms. In addition, in order to verify the effectiveness of the proposed method, this paper establishes a mathematical model with the minimum three-phase imbalance and commutation frequency as objectives and establishes a simulation model. The results of the simulation demonstrate that this method can successfully lower the three-phase imbalance of the low-voltage distribution network. It leads to a decrease of the main circuit's three-phase load imbalance rate from 27% to 6% and reduces each branch line's three-phase imbalance ratio to below 10%. After applying the method proposed in this paper, the main and branches circuit three-phase imbalance are both lower than the limit ratio of the LVDNs, which can improve the quality and safety of electricity consumption. Additionally, the results also prove that the commutation algorithm under this method has faster convergence speed, better application effect, and better stability, which has promotion and application value. [ABSTRACT FROM AUTHOR]

Nowdays, DNNs (Deep Neural Networks) are widely used in the field of DDoS attack detection. However, designing a good DNN architecture relies on the designer's experience and requires considerable work. In this paper, a GA (genetic algorithm) is used to automatically generate the DNN architecture for DDoS detection to minimize human intervention in the design process. Furthermore, given the complexity of contemporary networks and the diversity of DDoS attacks, the objective of this paper is to generate a DNN model that boasts superior performance, real-time capability, and generalization ability to tackle intricate network scenarios. This paper presents a fitness function that guarantees the best model generated possesses a specific level of real-time capability. Additionally, the proposed method employs multiple datasets to joint models generated, thereby enhancing the model's generalization performance. This paper conducts several experiments to validate the viability of the proposed method. Firstly, the best model generated with one dataset is compared with existing DNN models on the CICDDoS2019 dataset. The experimental results indicate that the model generated with one dataset has higher precision and F1-score than the existing DNN models. Secondly, model generation experiments are conducted on the CICIDS2017 and CICIDS2018 datasets, and the best model generated still performs well. Finally, this paper conducts comparative experiments on multiple datasets using the best model generated with six datasets and the best model generated by existing methods. The experimental results demonstrate that the best model generated with six datasets has better generalization ability and real-time capability. [ABSTRACT FROM AUTHOR]