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Ensuring sufficient mechanical performance while enabling lightweight design is critical for utilizing paper sandwich panels in the furniture industry. To design lightweight sandwich panels that balance mechanical properties and cost, this study developed a circular core paper sandwich panel (CCPSP) and investigated its structural efficiency using multi-objective optimization. The response surface method (RSM) based on Box–Behnken design was utilized to establish mathematical models relating the paper tube spacing, inner diameter, and height to the out-of-plane compressive strength, density, and cost. The resulting models effectively revealed the coupled effects of the parameters on the responses. Subsequently, the models were optimized using the non-dominated sorting genetic algorithm II (NSGA-II) to find the Pareto optimal trade-offs between maximizing compressive strength while minimizing its density and cost. The optimization solution resulted in an optimal set of paper tube geometries that maximized the structural efficiency of CCPSP. Overall, lower tube height conferred superior structural efficiency, while tube spacing and diameter were constrained. The results highlight the potential of CCPSP as an efficient and sustainable material for furniture manufacturing, enabled by multi-objective optimization of its structure. [ABSTRACT FROM AUTHOR]

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]

In the production of facial oil blotting papers, a certain number of papers must be counted before packaging. Currently, the papers are counted by hand, and this is hard work. Also, there are risks of adhesion of dust and wrinkles. In order to solve these problems, we propose a vision‐based approach. After the papers, which are arranged by shifting, are captured, the proposed image processing steps detect the boundaries of the papers. By counting the detected boundaries, the number of papers can be counted. Since the parameters for the proposed image processing are optimized by genetic algorithm, prior setting by a user is not necessary. For the experiments, an image dataset was constructed with six types of facial oil blotting papers. The proposed method achieved higher F‐measure than in other related works. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

As the increasing trend of global consumption of waste electrical and electronic equipment (WEEE), the recycling of WEEE has been thought highly of. In order to solve the problems of high economic cost, environmental detrimental caused by improper recycling, ignorance of social benefits of recycling, uncertain recycling amount and single recycling channel in recycling process of WEEE, this paper proposes a sustainable multi-objective WEEE double recycling channel fuzzy programming model based on triple bottom line (TBL) theory. First of all, TBL is used to develop sustainable multi-objective recycling models concerning economy, environment and society. Secondly, the model is fuzzed. This process includes two steps: the first step is to take WEEE recycling quantity as a triangular fuzzy parameter, and use fuzzy chance constraint method to transform fuzzy constraint into an equivalent clear condition; the second step is to fuzzify suboptimal objective in the model by defining objective membership degree, and transform multi-objective optimization problem into a single-objective optimization problem based on the maximum satisfaction. Finally, taking a recycling enterprise in Shantou, China, as an example, genetic algorithm (GA) and particle swarm optimization (PSO) are comparatively used to solve a calculation case related to the model and the contribution of the model is verified by comparing economic cost, environmental impact and social benefits of the dual recycling channels. [ABSTRACT FROM AUTHOR]

Due to the incoherent superposition of various color light transmission matrices (TMs) in space, it has always been a challenge to measure the multi-color light TMs directly or indirectly in the field of wavefront shaping. In this paper, a hybrid algorithm for indirectly measuring the TMs of multi-color light is proposed. The prominent feature of this algorithm is that the spatial and frequency domain information of the speckle patterns is simultaneously input into the multi-input fully connected neural network. Compared with previous spatial data-driven methods, our method can better learn the mapping relationships hidden behind the color samples, making the measurement of the TMs of multi-color light through the scattering media more accurate. Experimental results show that the enhancement factor of multi-point multi-color light focusing using the hybrid algorithm is improved by about 27% compared with the genetic algorithm, and the number of iterations is reduced by about 47%. The hybrid algorithm presents a new idea for multi-color light focusing, which is of great significance for wavefront shaping in complex optical fields. [ABSTRACT FROM AUTHOR]

Aiming to address the timing skew mismatch in the time‐interleaved analog‐to‐digital converter (TIADC) system, this paper presents a timing skew mismatch calibration method based on a back propagation (BP) neural network optimized by an adaptive genetic algorithm (AGA). In this paper, a trained BP neural network is used to detect the timing skew mismatch in the TIADC system, and the variable delay line is used to calibrate it. In this paper, AGA is used to optimize the BP neural network, accelerating its training speed and improving the detection accuracy of timing skew mismatch in the system. The proposed approach boasts superior detection speed and accuracy compared to other methods. In this paper, an 18‐bit 1GS/S 4‐channel TIADC system is simulated and the timing skew mismatch in the system is corrected. Simulation results show that the proposed calibration method has fast detection speed, high detection accuracy, and calibration accuracy. After completing the timing skew mismatch correction, the performance of the TIADC system is dramatically improved. The effective number of bits (ENOB) of the system increases by 9.5 bits, and the spurious‐free dynamic range (SFDR) increases by 59.9 dB. [ABSTRACT FROM AUTHOR]

In order to eliminate the nonlinear aerodynamic behaviour of a symmetric airfoil within the low angles of attack at low Reynolds number, this paper establishes a low Reynolds number airfoil optimization design procedure based on Gaussian process regression model and NSGA-II multi-objective genetic algorithm coupled with RANS numerical method. Then this paper carries out the airfoil optimization design with the average cruise factor in the range of small angle of attack of multiple states as the objective function. The results show that the optimized airfoil exhibits pronounced geometric bump and effectively alters the position and type of the laminar separation bubble. The aerodynamic characteristics of the airfoil are significantly improved, and the nonlinear phenomenon of the lift coefficient within the small angle of attack range at Re=4×104 basically disappears. Further analysis reveals that the bump of the optimized airfoil enhances the convective influence of the wall, which enhances the resistance of the incoming flow to the upward pressure gradient, thus improving the airfoil aerodynamic characteristics. [ABSTRACT FROM AUTHOR]

To solve the flexible job shop dynamic scheduling problem with fuzzy quality control time, which is more realistic, this paper established two rescheduling models for machine breakdown and urgent order insertion with the objective of minimizing completion time. Two models were for three failure scenarios: emergency order insertion, machine failure during no-load operation, and machine failure during work-piece processing. To solve this model, this paper designed a novel genetic-neighborhood search algorithm that integrated cellular-automata-based neighborhood search and random restart hill-climbing. Aiming at the characteristics of dual sub-decisions involving order sequencing and machine selection, this paper designed a two-layer coding scheme including process code and machine code, and designed corresponding genetic operations such as crossover and mutation for them. After that, this paper applied the genetic operations into the framework of cellular-automata-based neighborhood search to enhance the algorithm's global search capability, and integrated the random restart hill-climbing algorithm based on key operations to improve the algorithms local development capability. Experiments tested 10 flexible job shop scheduling instances and one flexible job shop scheduling instance with fuzzy quality control time to verify the effectiveness of the proposed algorithm and models. In addition, the test results under different failure scenarios show the proposed method outperforms the backward-based scheduling strategy used in practice. [ABSTRACT FROM AUTHOR]

The combination processing of molds and electronic product joint inspection introduce the mandatory concurrent operations on the same machine (MCDSM), which breaks the constraint that the same machine cannot process different jobs at the same time in the job shop scheduling (JSP) To solve this type of job shop scheduling problem, this paper proposed an adaptive hybrid initialization genetic algorithm (AHIGA). Firstly, this paper defined the problem as a generalized JSP considering mandatory concurrent operations on the same machine (GJSPMCO) problem, and established an optimization model using mixed integer programming with the objective of minimizing the maximum completion time. Next, it designed corresponding encoding and decoding techniques to support the representation and resolution of feasible scheduling plans within the constraints of MCOSM. Meanwhile, it established a population hybrid initialization method to generate high-quality feasible solutions. This paper also designed novel crossover and mutation operation methods to ensure the feasibility of newly generated solutions under the constraint of MCOSM. Additionally, it constructed an adaptive crossover and mutation operators to enable the adaptive updating of offspring, thereby enhancing the algorithms global search capability. Finally, it constructed 40 test cases based on JSP benchmark, and used these test cases and an example of joint inspection of electronic products for experiments. The results show that the proposed model and algorithm can effectively solve GJSPMCO problem, and the improvement strategies can enhance the quality of solutions, demonstrating the feasibility of the model and the superiority of AHIGA. [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]

Pressurized irrigation network systems constitute an important water-saving approach, but their relatively high costs limit their promotion. Pipe diameter optimization is the most direct way to reduce irrigation costs and can bring significant economic benefits. In recent years, material technology developments have improved networks' impact resistance, resulting in traditional flow velocity constraints no longer applying to current network designs, and appropriately increasing the velocity has become an engineering requirement. In addition, with the update of engineering construction standards, the location of pipe diameter changes is no longer limited to pipe section connections and is allowed to change between standard pipes. Therefore, this paper removes the constraint of the upper limit of flow velocity, defines it as a factor related to the maintenance cost of the network, and establishes a pipe section optimization model. Further, this paper changes the minimum optimization unit from pipe sections to standard pipes and establishes a standard pipe optimization model. The results show that compared with traditional optimization methods, the pipe section optimization model that deletes the upper velocity limit can reduce costs by 5.1%, in which the velocity will not increase infinitely but will be stable within a reasonable range. The standard pipe optimization model can reduce irrigation costs by 12.1%. It is worth noting that the two models were optimized to balance the head loss; their energy costs did not change significantly with the reduction in pipe diameter. In addition, this paper changes the structure of the traditional algorithm and designs a structure of double selection and parallel variation, achieving a faster and more accurate solution than the classic genetic algorithm. Practical Applications: Smart irrigation is an irrigation method based on advanced technology and data analysis that can manage water resources more accurately and efficiently. As an essential part of smart irrigation, pressure networks have the characteristics of saving water resources, improving the water utilization coefficient, and facilitating management and control. Their promotion can promote the development of agriculture. However, high network costs limit the popularity of smart network irrigation. Therefore, this study is based on the latest material technology, cancels the upper limit of flow velocity, and establishes a pipe section optimization model. Furthermore, this paper changes the minimum optimization unit from pipe sections to standard pipes and establishes a standard pipe optimization model. The research results show that the standard pipe optimization model can significantly reduce irrigation costs. This has practical significance for increasing farmers' income and promoting the development of smart pipeline irrigation. In addition, this paper changes the structure of the traditional algorithm and designs a structure of double selection and parallel variation, achieving a faster and more accurate solution than the classic genetic algorithm. [ABSTRACT FROM AUTHOR]

The relative rotation angle between two cabins should be automatically and precisely obtained during automated assembly processes for spacecraft and aircraft. This paper introduces a method to solve this problem based on distributed vision, where two groups of cameras are employed to take images of mating features, such as dowel pins and holes, in oblique directions. Then, the relative rotation between the mating flanges of two cabins is calculated. The key point is the registration of the distributed cameras; thus, a simple and practical registration process is designed. It is assumed that there are rigid and scaling transformations among the world coordinate systems (WCS) of each camera. Therefore, the rigid-correct and scaling-correct matrices are adopted to register the cameras. An auxiliary registration device with known features is designed and moved in the cameras' field of view (FOV) to obtain the matrix parameters so that each camera acquires traces of every feature. The parameters can be solved using a genetic algorithm based on the known geometric relationships between the trajectories on the registration devices. This paper designs a prototype to verify the method. The precision reaches 0.02° in the measuring space of 340 mm. [ABSTRACT FROM AUTHOR]

Efficient specialized vehicle cooperative scheduling is significant for airport operations, particularly during times of high traffic, which reduces the risk of flight delays and increases customer satisfaction. In this paper,we construct a multi-type vehicles collaborative scheduling model with the objectives of minimizing vehicle travel distance and vehicle waiting time. Additionally, a three-layer genetic algorithm is designed, and the crossover and mutation operations are enhanced to address the scheduling model. Due to the numerous uncertainties and stochastic interferences in airport operations, conventional scheduling methods unable to effectively address these challenges, this paper combines improved genetic algorithm, simulation algorithm, and digital twins technology, proposing a multi-strategy scheduling framework for specialized vehicles based on digital twins. The scheduling framework utilises digital twins to capture dynamic data from the airport and continuously adjusts the scheduling plan through the scheduling strategy to ensure robust scheduling for specialized vehicles. In the event of severe delays at the airport, fast and efficient re-scheduling can be achieved. Finally, the effectiveness of the proposed scheduling framework is validated using domestic flight data, and extensive experiments and analyses are conducted in different scenarios. This research contributes to addressing the optimization problem of cooperative scheduling for multi-type vehicles at airports. [ABSTRACT FROM AUTHOR]

Energy saving has become a key concern of metro systems to reduce operating costs and meet the requirements of sustainable development. Energy-efficient timetabling, an effective way to reduce traction energy consumption, has gotten much attention in recent decades. However, limited research about energy-efficient timetabling simultaneously considered realistic speed profiles and the influence of train loads, which change greatly in different sections and significantly affect energy consumption. Therefore, this paper proposes an energy-efficient timetabling approach to improve regenerative energy utilization based on varying train loads and realistic speed profiles for bidirectional metro lines. First, based on the operational and safety constraints, this paper constructs an integer energy-efficient timetabling model where dwell times and nonuniform headways are taken as decision variables. Besides, the turnaround process is also considered to ensure rolling stock circulation efficiency. Sectional passenger volumes are employed to determine the varying train loads. Energy consumption is calculated based on realistic speed profiles. Owing to the complexity of the model, an improved adaptive large neighborhood search (ALNS) algorithm is designed to solve this model according to its characteristics. Finally, numerical examples are conducted based on the practical data from Shanghai Metro Line 17. The results indicate that the proposed approach effectively solves the energy-efficient timetabling problem with varying train loads and realistic speed profiles for bi-directional metro lines. The approach is suitable for off peak hours and can reduce energy consumption effectively, and the reduction is up to 11.9% for the presented cases. The improved ALNS algorithm can find good solutions within reasonable times in different cases. Moreover, compared with the widely used genetic algorithm in the literature, the improved ALNS algorithm is more efficient in solving the proposed energy-efficient timetabling problem in different cases. [ABSTRACT FROM AUTHOR]

AbstractGrain is the root of the people, and grain storage is a top priority. It is necessary to improve the efficiency of grain storage operations and reduce work intensity, so it is important to develop an automated or even intelligent grain leveling robot for the precise operation of grain silos. In this paper, we propose an area classification method based on target leveling height for the special working mode of truss-type grain leveling robot, simplify the 3D map to a 2D map, reduce the difficulty of path planning, and improve the working efficiency. Based on multi-level path planning and genetic algorithm to achieve the planning of the working path of the grain-leveling robot, it solves the problem of a large number of useless trips under the adoption of full-coverage operation. It saves a lot of time, improves grain-leveling efficiency, reduces energy consumption, optimizes the effect of grain-leveling, and helps realize the precise storage operation of grain silos. The experiment of rough leveling operation was carried out through the grain leveling robot prototype and the simulated experimental warehouse, and the results show that the height difference between the peak of the grain surface and the target leveling height is within 5 cm, which verifies that the path planning method in this paper is feasible, and shows that the grain leveling robot can complete the task of grain leveling and the effect is good. [ABSTRACT FROM AUTHOR]

Potash plays an important role in agriculture and food security, so does the estimation of the content of substances in potash. Chemical methods for such estimation tasks are time-consuming, whereas thermal infrared hyperspectral (TIH) images provide us with an opportunity to do estimation efficiently. This paper hence applies the TIH technology to predict the content of various substances in potash. On the one hand, the TIH technology provides rich hyperspectral information for substance discovery and analysis. On the other hand, it introduces challenges of dimensionality and information redundancy, leading to potential decreased predicting accuracy. To solve the problem, as well as easing the burden of detection instruments in terms of band usage, band selection is critical. This paper proposes a dynamic metric-guided particle swarm optimization (DM-GPSO) method for band selection. It adaptively adjusts parameters to prevent local optima, avoiding the need for manual parameter tuning. Variety of band selection algorithms, including the genetic algorithm (GA), particle swarm optimization (PSO), and improved PSO algorithms, are tested to extract and analyse the characteristic bands of potash. Experimental results demonstrate that the TIH technology combined with a proper band selection methodis effective in estimating the content of potash. The new DM-GPSO can select different combinations of bands, which lead to better and more stable prediction results. The analysis on the combinations of selected bands also reveals that picromerite and potassium chloride have similar intervals of characteristic bands, which can be used to guide the production process of potash in practice. [ABSTRACT FROM AUTHOR]

A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy. [ABSTRACT FROM AUTHOR]

The control of the maintenance of the industrial installations, in particular of the costs due to the implementation of the preventive policies is very interesting because of the growing importance of this service in the chains of production. The objective of this paper is to minimize the preventive maintenance costs of a production unit. For this, a state of the art on the maintenance cost models according to the policy used is first made, then a synthesis of the optimization methods is made in order to deploy the exact resolution methods and the genetic algorithms. The result of this paper is the proposal of a cost model corresponding to a periodic maintenance policy with minimal repair to the failure and the optimization of the periodicities of the partial revisions of the production unit. [ABSTRACT FROM AUTHOR]

A novel two-dimensional Quad-stable stochastic resonance (NTDQSR) system is proposed in this paper to address the poor signal detection capability of the original one-dimensional Quad-stable stochastic resonance (ODQSR) system. Firstly, expressions for the equivalent potential function and the steady-state probability density (SPD) function of the system are derived to study the impact of parameters on it. Secondly, the system is simulated numerically, and the performance of NTDQSR system is evaluated under trichotomous noise in this paper, because trichotomous noise exists widely in practical applications. Finally, the genetic algorithm (GA) is used to optimize the system parameters, and NTDQSR system is applied to bearing fault detection. In LDK UER204 ring fault detection, MSNRG in NTDQSR system is 0.9927 dB and 0.4805 dB higher than ODQSR and under-damped Quad-stable stochastic resonance (UQSR) system. In the outer ring experiment on the experimental bench at CWRU, MSNRG in NTDQSR system is 0.7255dB and 0.0734dB higher than the other two systems, while in the inner ring experiment, MSNRG is 0.6535dB and 0.1943dB higher than the other two systems, respectively. NTDQSR system is compared with ODQSR and UQSR systems to verify the good application value of NTDQSR system in practical engineering. [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]

This paper proposes a ResGA-Net algorithm for cloud top height (CTH) retrieval using FY-4A satellite data. The algorithm utilizes genetic algorithms for data selection and employs a residual module-based neural network for modeling. It takes the spectral channel data from the FY-4A satellite as input features and uses CTH extracted from ground-based millimeter-wave cloud radar reflectivity as the target. By combining the large observation scale of the FY-4A satellite and the high accuracy of ground-based cloud radar observations, the model can generate satellite CTH products with higher precision. To validate the effectiveness of the algorithm, experiments were conducted using data from the Beijing area spanning from January 2020 to January 2022. The experimental results show that the metrics of the proposed ResGA-Net outperform those of various contrastive algorithms, and compared to the original FY-4A CTH product, the RMSE and MAE have decreased by 37.89% and 34.77%, while the PCC and SRCC have increased by 11.17% and 9.47%, respectively, demonstrating the superiority of the proposed method presented in this paper. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) systems generate a vast volume of data to process, and the volume of data generated by the IoT will continue to expand exponentially in the future. Due to the large-scale nature of such systems and their geographically distributed nature, how to optimally place operators and brokers for data processing across cloud, fog, and edge is the main concern of this paper. Furthermore, in the past, IoT systems relied on cloud-based brokers. If the communication between operators in an IoT application has to be regulated by a broker located in the cloud, the advantages brought by the processing of nodes located at the edge and the fog may be neglected. We consider various factors, such as the impact of different network topologies on operators and brokers, the needs of IoT application users, the performance of sensor layers, and the location characteristics of IoT sensors. In diverse placement strategies, the characteristics of the broker have been given special attention. This paper considers the combined architecture of the operator and broker on the edge, fog, and cloud as an optimization problem of minimizing computational processing and data transmission cost. We propose jointly placing the IoT application's operator and broker on the edge, fog, and cloud nodes. Then we proposed two algorithms: the low-complexity heuristic algorithm named GREEDY and the genetic algorithm called GA. Simulation results show that, in most situations, the GA placement method outperforms others and provides a near-optimal solution. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to the fuzzy newsvendor problem for inventory management applications. The main contributions of the paper are the following: a new credibility estimation is proposed, to explore the neighborhood around the most impactful demand scenarios; a simulation procedure was designed for the different demand scenarios, which allows comparison of the proposed approach with classical and fuzzy multi-item newsvendor problems; a modified genetic algorithm (GA) is introduced to ameliorate previous genetic algorithms in both the generation and evaluation of solutions. The new formulation of the fuzzy newsvendor problem, together with the modified GA, were shown to improve the average profit by up to 55% in problems with low-budget scenarios. [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]

This paper proposed a two-dimensional composite square lattice structure containing two kinds of inclusions (polymethylmethacrylate and T2 copper). To maximize the relative widths of the gaps between the adjacent energy bands of the phononic crystals (PnCs), an improved multi-parameter genetic algorithm was adopted in this paper. The material distribution and ligament sizes were considered simultaneously by ternary encoding and binary encoding. The propagation wave behaviors of the composite lattice structures were studied by the finite element method. The effects of different lattice shapes and other relevant influencing parameters on the bandgaps were discussed. The results showed that the lattice shape, ligament width, and material density affect the width and the location of the bandgaps, and the effectiveness of the proposed method was demonstrated by a transmission spectrum experiment. [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]

Damage detection and localization in civil engineering constructions using dynamic analysis has become an important topic in recent years. This paper presents a methodology based on non-destructive detection, localization and quantification of multiple damages in simple and continuous beams, and a more complex structure, namely two-dimensional frame structure. The proposed methodology makes used of Firefly Algorithm and Genetic Algorithm as optimization tools and the Coordinate Modal Assurance Criterion as an objective function. The results show that the proposed combination of Coordinate Modal Assurance Criterion and Firefly Algorithm or Genetic Algorithm can be easily used to identify multiple local structural damages in complex structures. However, the convergence rate becomes slower for the case of multiple damages compared to the case of single damage. The effect of noise on the algorithm is further investigated. It is found that the proposed technique is able to detect the damage location and its severity with high accuracy in the presence of noise, although the convergence rate became slower than in the case when no noise is present. It is also found that the convergence rate of Firefly Algorithm is much faster than that of Genetic Algorithm. [ABSTRACT FROM AUTHOR]

3D integration is one of the scalable multiprocessor design solutions. The main challenge in 3D design is temperature traps, especially in the upper layers and hot spots. The current studies present solutions for solving temperature and hotspot problems in topology, routing, and mapping levels. Accordingly, the characteristics of temperature-aware core mapping are beneficial for dealing with the challenges of 3D chips. In this paper, the core temperature is modeled based on processing, communication power, neighbor node temperature, distance from the heat sink, and hotspot node. To find the optimal solution, this paper proposes thermal-aware application mapping techniques using genetic algorithms and fuzzy logic to minimize peak temperatures in 3D network-on-chip (NoC) architectures. Fitness functions include the above parameters with the same weight (GSM) and the fitness function with fuzzy logic by considering the effect of distance from heat sink (GFM1) and heat sink distance and the number of neighbor's core (GFM2). Specifically, the fuzzy logic-based GFM1 algorithm demonstrates superior performance over the genetic algorithm-driven GSM approach in reducing power consumption and energy costs associated with inter-core communication traffic. Across the different traffic patterns tested, GFM1 consistently achieves lower energy expenditure, with reductions upward of 50% compared to GSM. This underlines the strengths of the fuzzy technique in enabling thermal-aware mapping to minimize chip temperatures in the presence of intensive workloads and communication. In essence, the key takeaway is that GFM1 outperforms GSM in the context of power and energy metrics linked to handling on-chip traffic between processing cores. [ABSTRACT FROM AUTHOR]

A new methodology for solving the fuzzy multiobjective optimization problems is proposed in this paper by considering the fusion of cooperative game theory and genetic algorithm. The original fuzzy multiobjective optimization problem needs to be transformed into a scalar optimization problem, which is a conventional optimization problem. Usually, the assignments of suitable coefficients to the corresponding scalar optimization problem are subjectively determined by the decision makers. However, these assignments may cause some biases by their subjectivity. Therefore, this paper proposes a mechanical procedure to avoid this subjective biases. We are going to formulate a cooperative game using the α -level functions of the multiple fuzzy objective functions. Under this setting, the suitable coefficients can be determined mechanically by involving the core values of the cooperative game, which is formulated using the multiple fuzzy objective functions. We shall prove that the optimal solutions of the transformed scalar optimization problem are indeed the nondominated solutions of fuzzy multiobjective optimization problem. Since the core-nondominated solutions will depend on the coefficients that are determined by the core values of cooperative game, there will be a lot of core-nondominated solutions that will also depend on the corresponding coefficients. In order to obtain the best core-nondominated solution, we shall invoke the genetic algorithms by evolving the coefficients. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is a crucial preprocessing step that aims to eliminate irrelevant and redundant features, reduce the dimensionality of the feature space, and enhance clustering efficiency and effectiveness. FS is categorized as NP-Hard due to the high number of existing solutions. Various metaheuristic methods have been developed to address the FS problem, yielding promising results. Particularly, particle swarm optimization (PSO), an evolutionary computing (EC) approach guided by swarm intelligence, has gained widespread adoption owing to its implementation simplicity and potential for global search. This paper analyzes several variants of PSO algorithms and introduces a new FS method called HPSO. The proposed approach utilizes an asynchronously adaptive inertia weight and an improved constriction factor. Additionally, it incorporates a chaotic map and a MAD fitness function with a feature count penalty to tackle the clustering FS problem. The efficiency of the developed method is evaluated against the genetic algorithm (GA) and well-known variants of PSO algorithms, including PSOs with fixed inertia weights, PSOs with improved inertia weights, PSOs with fixed constriction factors, PSOs with improved constriction factors, PSOs with adaptive inertia weights, and PSO's includes advanced learning exemplars and sophisticated structure topologies. This paper assesses two different reference text data sets, Reuters-21578 and Webkb. In comparison with competitive methods, the proposed HPSO method achieves higher clustering precision and selects a more informative feature set. [ABSTRACT FROM AUTHOR]

An optimization model that may be applied to analyze building retrofit strategies is presented in this research. The aim of this research paper is to identify the optimal thermal envelope configuration that will assure the minimum energy requirement for heating in the case of a residential building, while also considering price restrictions obtained through a specific market survey. To achieve this, several values for the following parameters are considered: thermal insulation materials' conductivities and thicknesses, windows' overall heat transfer coefficients and total solar energy transmittance and doors' thermal proprieties. Additionally, this paper presents a method used to find the best option from among the available heat pumps that could cover most of the energy requirements for heating and domestic hot water systems, also considering the products' prices. The proposed method is based on a Non-dominated Sorting Genetic Algorithm II (NSGA-II) model developed in the Pymoo (Multi-Objective Optimization in Python) library. The result shows that the energy requirement for heating can be reduced by up to approximately 75% compared to that obtained in the case of a non-insulated building by using suitable insulation materials and doors and windows with superior thermal proprieties chosen by the NSGA-II. [ABSTRACT FROM AUTHOR]

The Bouc–Wen nonlinear hysteretic model is widely used in nonlinear systems, and it is difficult to identify the parameters of Bouc–Wen model because of its high nonlinearity. In the paper, a hybrid genetic algorithm combined with sequential quadratic programming is proposed to solve the problem about the parameter identification of Bouc–Wen model. Sequential quadratic programming method has good local convergence and superlinear convergence rate. Therefore, compared with the genetic algorithm, the proposed identification method can accelerate the convergence speed in parameter recognition of Bouc–Wen model and achieve excellent recognition accuracy. Through the function test and the numerical simulation of the shear structure, it is proved that the proposed method has high convergence speed and accurate identification. In addition, the theory and method proposed in this paper are combined with Bouc–Wen model to identify parameters of overall restoring force model of the energy-dissipation structures, the identification results are in good agreement with the experimental results, verifying reliability and accuracy of the model and robustness of the proposed identification method. [ABSTRACT FROM AUTHOR]

The popularity of the average current mode (ACM) controlled boost type power factor correction (PFC) topologies is increasing due to their suitability for power quality problems. Proportional-Integral (PI) controller method is generally used in ACM controlled boost PFC circuits which include two controllers. However, the most important complexity of ACM controlled PFC circuits is tuning the coefficients of the PI controllers optimally. Therefore, this paper proposes the optimal tuning of PI coefficients used in ACM controlled boost PFC circuits using different meta-heuristics algorithms. First, the proposed ACM controller-based boost PFC topology is analyzed in MATLAB/Simulink software by using variable loads. Then the simulation results of the Cuckoo Optimization Algorithm (COA) based ACM controlled boost PFC converter are compared with the results determined via Ziegler-Nichols (ZN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Imperialistic Competitive Algorithm (ICA), and Invasive Weed Optimization (IWO). Finally, the experimental verification of the topology has been done using a 600 W prototype and eZdsp F28335. As COA showed better results among other evolutionary algorithms used in this paper, we used COA parameters to observe the performance of the proposed tuning method. The experimental studies have been done under different load variations similar to the simulation studies. [ABSTRACT FROM AUTHOR]

The energy conversion efficiency of wave energy device is one of the key problems in the large-scale utilization of wave energy. The study of the maximum output POWER of PTO (Power-Take-Off) system provides a theoretical reference for the efficient utilization of energy. In this paper, genetic algorithm and adaptive differential evolution algorithm based on neighborhood search are used to optimize the two-objective multi-order differential equations for the maximum output power of PTO system, and the global optimal solution is obtained. Compared with the traversal algorithm, the algorithm involved in this paper is efficient and the optimal output power obtained can provide a scientific basis for the structural optimization design and material selection of the wave energy device. [ABSTRACT FROM AUTHOR]

This paper focuses on building a ground motion model (GMM) using artificial neural network technique driven by a synthetically generated database in a data poor region. A hybrid simulated method considering nonlinear site response is used to generate potential ground motions in Tianjin area. The synthetic database containing a total of 9267 ground motion records from 93 simulated earthquake events are utilized to develop the model. A back propagation neural network developed by genetic algorithm (GA-BPNN) is used to obtain the optimal weights and biases that most fit the target dataset without overfitting. The structure of GA-BPNN model consists of 322 unknowns including weights and biases that connect input and output parameters. The model is developed to predict PGA and 5% damped spectral acceleration (periods from 0.01 to 10 s). The input parameters consist of moment magnitude (Mw), strike (φ), dip (δ), rake angle (λ), focal depth (h), epicenter distance (R) and azimuth (θ). The model performance is observed to be within the appropriate error limits. It is found that the model has the ability to capture the parametric variation relationship. Additionally, the synthetically based model is compared with empirically based GMM relations derived from NGA-West2, finding it has similar performance and behavior to leading GMMs. The model application in Tianjin area demonstrates that this paper provides a scheme to utilize synthetic database and build GMMs in data poor regions. [ABSTRACT FROM AUTHOR]

Given the increasing severity of energy shortages, the exploration of effective strategies to optimize energy structures has become imperative. This requires careful consideration of energy consumption patterns, especially since these data are fundamental inputs for policy formulation. Given the uncertainty in the rate of change in energy consumption, this paper proposes an optimized multivariable grey Bernoulli model that is rooted in the grey Bernoulli model and incorporates background values and genetic algorithms. The grey Bernoulli model effectively linearizes nonlinear problems, thus simplifying computational procedures. In addition, to account for random fluctuations of relevant factors that may affect the model's predictions, this model introduces nonlinear correction terms that allow simulation and prediction values to adhere to the grey index law. The incorporation of background values enhances the model's ability to process information, providing it with a superior grasp of real data. Genetic algorithms can be used to refine the model's parameters, increasing its adaptability and precision. Finally, this paper applies the refined model to examine China's energy consumption patterns, validating its efficacy and versatility. Furthermore, energy consumption patterns for the next four years are forecast, with the analysis revealing that the growth rate of energy consumption from 2021 to 2024 shows a downward trend, particularly notable in 2024, where the growth rate is 1.64%. [ABSTRACT FROM AUTHOR]

Understanding air quality requires a comprehensive understanding of its various factors. Most of the association rule techniques focuses on high frequency terms, ignoring the potential importance of low- frequency terms and causing unnecessary storage space waste. Therefore, a dynamic genetic association rule mining algorithm is proposed in this paper, which combines the improved dynamic genetic algorithm with the association rule mining algorithm to realize the importance mining of low- frequency terms. Firstly, in the chromosome coding phase of genetic algorithm, an innovative multi-information coding strategy is proposed, which selectively stores similar values of different levels in one storage unit. It avoids storing all the values at once and facilitates efficient mining of valid rules later. Secondly, by weighting the evaluation indicators such as support, confidence and promotion in association rule mining, a new evaluation index is formed, avoiding the need to set a minimum threshold for high-interest rules. Finally, in order to improve the mining performance of the rules, the dynamic crossover rate and mutation rate are set to improve the search efficiency of the algorithm. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the unit point multi-information coding strategy in reducing the rule storage air, the effectiveness of mining the rules formed by the low frequency item set, and the effectiveness of combining the rule mining algorithm with the swarm intelligence optimization algorithm in terms of search time and convergence. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the above three aspects. The unit point multi-information coding strategy reduced the rule space storage consumption by 50%, the new evaluation index can mine more interesting rules whose interest level can be up to 90%, while mining the rules formed by the lower frequency terms, and in terms of search time, we reduced it about 20% compared with some meta-heuristic algorithms, while improving convergence. [ABSTRACT FROM AUTHOR]

Long-duration radar signals have a low-range resolution but have high-energy contents, whereas short-duration radar signals have a high-range resolution but have low-energy contents. The pulse compression technique allows the radar signal designers to achieve desired energy corresponding to long-duration signals and range resolution corresponding to short-duration signals simultaneously. The pulse compression technique is used in a high-range resolution radar to discriminate closely located targets in the same vicinity and it also improves radar parameters' measurement accuracy. Polyphase codes are widely used as pulse compression signals in high-range resolution radar systems. The major disadvantage of pulse compression codes is unwanted correlation sidelobes at the output of the matched filter. This paper proposes the sidelobe reduction technique to reduce the Peak Sidelobes Level (PSL) of Frank polyphase codes. The weights of the proposed Sidelobes Reduction Filter (SRF) are optimized by using the Modified Genetic Algorithm (MGA). The proposed technique reduced autocorrelation peak sidelobe level significantly. Finally, the synthesized results of the proposed SRF are presented in this paper. [ABSTRACT FROM AUTHOR]

Since financial assets on stock exchanges were created, investors have sought to predict their future values. Currently, cryptocurrencies are also seen as assets. Machine learning is increasingly adopted to assist and automate investments. The main objective of this paper is to make daily predictions about the movement direction of financial time series through classification models, financial time series preprocessing methods, and feature selection with genetic algorithms. The target time series are Bitcoin, Ibovespa, and Vale. The methodology of this paper includes the following steps: collecting time series of financial assets; data preprocessing; feature selection with genetic algorithms; and the training and testing of machine learning models. The results were obtained by evaluating the models with the area under the ROC curve metric. For the best prediction models for Bitcoin, Ibovespa, and Vale, values of 0.61, 0.62, and 0.58 were obtained, respectively. In conclusion, the feature selection allowed the improvement of performance in most models, and the input series in the form of percentage variation obtained a good performance, although it was composed of fewer attributes in relation to the other sets tested. [ABSTRACT FROM AUTHOR]

In order to solve the deficiencies of traditional board-level assembly CubeSat, such as low modularity, complex assembly process and poor scalability, a sandwich module assembly structure and method with no drive components is proposed, which can achieve quick connection and flexible assembly between modules. First, the proposed CubeSat standard module can be compatible with the standard interface of traditional CubeSat. The 4-in-1 interface of Mechanical-Electrical-Thermal-Data does not occupy the internal space of the standard module, and the electrical and thermal interfaces meet the requirements of CubeSat in-orbit work. Secondly, in order to improve the reliability of the connection between the modules, a plug-in connection scheme of the mechanical interface was designed. Finally, a genetic algorithm was used to optimize the interface size, so that it has better plug-in performance. The simulation results show that the performance of the male interface of titanium alloy material is the best, and the error between the finite element analysis and genetic algorithm results is less than 4.8 %, which is consistent with the actual tensile test results of the optimized prototype, and meets the mechanical requirements of the launch process and space orbit operation for CubeSat. The test results can guide production and provide a reference for subsequent engineering applications of CubeSat modular assembly. • In this paper, a standardized modular assembly method of CubeSat sandwich structure is designed. The assembly structure does not include driving parts, is simple in form, can be quickly connected, is flexible in assembly mode, and does not occupy the internal space of the standard module. • A standard 4-in-1 connection device was designed in this paper, including four interfaces: mechanical, electrical, thermal and data, which can achieve "plug and play". • This paper uses genetic algorithm to optimize the structure of the mechanical connection male joint, and obtains the optimal configuration of the male joint. This ensures the reliability, firmness and stability of mechanical connections, and meets the mechanical requirements of CubeSats during launch and in orbit. [ABSTRACT FROM AUTHOR]

The scheduling of disassembly lines is of great importance to achieve optimized productivity. In this paper, we address the Hybrid Disassembly Line Balancing Problem that combines linear disassembly lines and U-shaped disassembly lines, considering multi-skilled workers, and targeting profit and carbon emissions. In contrast to common approaches in reinforcement learning that typically employ weighting strategies to solve multi-objective problems, our approach innovatively incorporates non-dominated ranking directly into the reward function. The exploration of Pareto frontier solutions or better solutions is moderated by comparing performance between solutions and dynamically adjusting rewards based on the occurrence of repeated solutions. The experimental results show that the multi-objective Advantage Actor-Critic algorithm based on Pareto optimization exhibits superior performance in terms of metrics superiority in the comparison of six experimental cases of different scales, with an excellent metrics comparison rate of 70%. In some of the experimental cases in this paper, the solutions produced by the multi-objective Advantage Actor-Critic algorithm show some advantages over other popular algorithms such as the Deep Deterministic Policy Gradient Algorithm, the Soft Actor-Critic Algorithm, and the Non-Dominated Sorting Genetic Algorithm II. This further corroborates the effectiveness of our proposed solution. [ABSTRACT FROM AUTHOR]