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With the continuous increase in the number of in-orbit satellites and the explosive growth in the demand for observation targets, satellite resource allocation and mission scheduling are faced with the problems of declining benefits and stagnant algorithm performance. This work proposes a progressive optimization mechanism and population size adaptive strategy for an improved differential evolution algorithm (POM-PSASIDEA) in large-scale multi-satellite imaging mission planning to address the above challenges. (1) MSIMPLTS based on Multi-layer Objective Optimization is constructed, and the MSIMPLTS is processed hierarchically by setting up three sub-models (superstructure, mesostructure, and understructure) to achieve a diversity of resource selection and step-by-step refinement of optimization objectives to improve the task benefits. (2) Construct the progressive optimization mechanism, which contains the allocation optimization, time window optimization, and global optimization phases, to reduce task conflicts through the progressive decision-making of the task planning scheme in stages. (3) A population size adaptive strategy for an improved differential evolution algorithm is proposed to dynamically adjust the population size according to the evolution of the population to avoid the algorithm falling into the local optimum. The experimental results show that POM-PSASIDEA has outstanding advantages over other algorithms, such as high task benefits and a high task allocation rate when solved in a shorter time. [ABSTRACT FROM AUTHOR]

Traditional path planning algorithms typically focus only on path length, which fails to meet the low energy consumption requirements for wall-climbing robots in bridge inspection. This paper proposes an improved sparrow search algorithm based on logistic–tent chaotic mapping and differential evolution, aimed at addressing the issue of the sparrow search algorithm's tendency to fall into local optima, thereby optimizing path planning for bridge inspection. First, the initial population is optimized using logistic–tent chaotic mapping and refracted opposition-based learning, with dynamic adjustments to the population size during the iterative process. Second, improvements are made to the position updating formulas of both discoverers and followers. Finally, the differential evolution algorithm is introduced to enhance the global search capability of the algorithm, thereby reducing the robot's energy consumption. Benchmark function tests verify that the proposed algorithm exhibits superior optimization capabilities. Further path planning simulation experiments demonstrate the algorithm's effectiveness, with the planned paths not only consuming less energy but also exhibiting shorter path lengths, fewer turns, and smaller steering angles. [ABSTRACT FROM AUTHOR]

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Targeting the issues of slow speed and inadequate precision of optimal solution calculation for multi-user detection in complex noise environments, this paper proposes a multi-user detection algorithm based on a Hybrid Cheetah Optimizer (HCO). The algorithm first optimizes the control parameters and individual update mechanism of the Cheetah Optimizer (CO) algorithm using a nonlinear strategy to improve the uniformity and discretization of the individual search range, and then dynamically introduces a differential evolutionary algorithm into the improved selection mechanism of the CO algorithm, which is utilized to fine-tune the solution space and maintain the local diversity during the fast search process. Simulation results demonstrate that this detection algorithm not only realizes fast convergence with a very low bit error rate (BER) at eight iterations but also has obvious advantages in terms of noise immunity, resistance to far and near effects, communication capacity, etc., which greatly improves the speed and accuracy of optimal position solving for multi-user detection and can achieve the purpose of accurate solving in complex environments. [ABSTRACT FROM AUTHOR]

At present, mining haul trucks (MHTs) directly deploy the on‐road heavy‐duty trucks' battery‐electric powertrain, as they can cut down costs and emissions in mining. However, the operating patterns of MHT are different, e.g., ultraduty, low‐speed, and continuous road slopes, resulting in a mismatch between the dynamic and economic performance of mining required and the MHT achieved. The powertrain design and control influence the dynamic and economic performance, which can be quantitatively measured by top speed, gradeability, and energy consumption. This study uses an improved differential evolutionary algorithm to develop an integrated optimization platform to obtain the components sizing, gear ratio, and shifting schedule for the dedicated battery‐electric MHT. Mathematical models are established and validated using on‐site experiment data. An integrated optimization platform is initiated by concurrently formulating the motor sizing, gear ratio, and shifting schedule and solved by the improved differential evolutionary algorithm. Optimization results indicate that the economic performance is enhanced by 10.82%, 11.08%, 11.18%, and 11.20%, respectively, while maintaining or slightly improving the dynamic performance. Besides, the achievable maximum speed at the most common grade is boosted by 11.82%, 6.52%, 7.44%, and 6.52%, respectively. The study provides an approach to developing a battery‐electric powertrain for MHT. [ABSTRACT FROM AUTHOR]

This paper presents a method for measuring stress fields within the framework of coupled data models, aimed at determining stress fields in isotropic material structures exhibiting localized deterioration behavior without relying on constitutive equations in the deteriorated region. This approach contributes to advancing the field of intrinsic equation-free mechanics. The methodology combines measured strain fields with data-model coupling driven algorithms. The gradient and Canny operators are utilized to process the strain field data, enabling the determination of the deterioration region's location. Meanwhile, an adaptive model building method is proposed for constructing coupling driven models. To address the issue of unknown datasets during computation, a dataset updating strategy based on a differential evolutionary algorithm is introduced. The resulting optimal dataset is then used to generate stress field results. Validation against finite element method calculations demonstrates the accuracy of the proposed method in obtaining full-field stresses in specimens with local degradation behavior.

With the continuous increase in the number of in-orbit satellites and the explosive growth in the demand for observation targets, satellite resource allocation and mission scheduling are faced with the problems of declining benefits and stagnant algorithm performance. This work proposes a progressive optimization mechanism and population size adaptive strategy for an improved differential evolution algorithm (POM-PSASIDEA) in large-scale multi-satellite imaging mission planning to address the above challenges. (1) MSIMPLTS based on Multi-layer Objective Optimization is constructed, and the MSIMPLTS is processed hierarchically by setting up three sub-models (superstructure, mesostructure, and understructure) to achieve a diversity of resource selection and step-by-step refinement of optimization objectives to improve the task benefits. (2) Construct the progressive optimization mechanism, which contains the allocation optimization, time window optimization, and global optimization phases, to reduce task conflicts through the progressive decision-making of the task planning scheme in stages. (3) A population size adaptive strategy for an improved differential evolution algorithm is proposed to dynamically adjust the population size according to the evolution of the population to avoid the algorithm falling into the local optimum. The experimental results show that POM-PSASIDEA has outstanding advantages over other algorithms, such as high task benefits and a high task allocation rate when solved in a shorter time.

Traditional path planning algorithms typically focus only on path length, which fails to meet the low energy consumption requirements for wall-climbing robots in bridge inspection. This paper proposes an improved sparrow search algorithm based on logistic–tent chaotic mapping and differential evolution, aimed at addressing the issue of the sparrow search algorithm’s tendency to fall into local optima, thereby optimizing path planning for bridge inspection. First, the initial population is optimized using logistic–tent chaotic mapping and refracted opposition-based learning, with dynamic adjustments to the population size during the iterative process. Second, improvements are made to the position updating formulas of both discoverers and followers. Finally, the differential evolution algorithm is introduced to enhance the global search capability of the algorithm, thereby reducing the robot’s energy consumption. Benchmark function tests verify that the proposed algorithm exhibits superior optimization capabilities. Further path planning simulation experiments demonstrate the algorithm’s effectiveness, with the planned paths not only consuming less energy but also exhibiting shorter path lengths, fewer turns, and smaller steering angles.

Energy consumption forecasting plays an important role in energy management, conservation, and optimization in manufacturing companies. Aiming at the tin smelting process with multiple types of energy consumption and a strong coupling with energy consumption, the traditional prediction model cannot be applied to the multi-output problem. Moreover, the data collection frequency of different processes is inconsistent, resulting in few effective data samples and strong nonlinearity. In this paper, we propose a multi-kernel multi-output support vector regression model optimized based on a differential evolutionary algorithm for the prediction of multiple types of energy consumption in tin smelting. Redundant feature variables are eliminated using the distance correlation coefficient method, multi-kernel learning is introduced to improve the multi-output support vector regression model, and a differential evolutionary algorithm is used to optimize the model hyperparameters. The validity and superiority of the model was verified using the energy consumption data of a non-ferrous metal producer in Southwest China. The experimental results show that the proposed model outperformed multi-output Gaussian process regression (MGPR) and a multi-layer perceptron neural network (MLPNN) in terms of measurement capability. Finally, this paper uses a grey correlation analysis model to discuss the influencing factors on the integrated energy consumption of the tin smelting process and gives corresponding energy-saving suggestions. [ABSTRACT FROM AUTHOR]

This research addresses a vehicle routing problem with simultaneous pickup and delivery with time windows from multiple depots (MVRPSPDTW) over a time horizon in the B2C e-commerce logistics system. We consider an e-commerce logistics system with a multi-period, which consists of customers, logistics service providers (LSPs), suppliers, and a decision-making platform. A mixed-integer non-linear programming (MINLP) model is developed and tested on small- and large-scale instances. To handle more realistic large-scale problems, we have used two approaches (i) exact optimisation approach using (i.e. CPLEX tool) and metaheuristic algorithms (i.e. Differential Evolutionary Algorithm (DE), Parallel Differential Evolutionary Algorithm (Par-DE), Genetic Algorithm (GA), and Block-based Genetic Algorithm (BBGA)) to minimise the total transportation cost and penalty due to the delay by logistics service providers. The computation experiment is conducted on the real practical scenario data and the comparative result is demonstrated. [ABSTRACT FROM AUTHOR]

With the rapid development of tourism, the rapid growth in the number of tourists caused by the imbalance of the passenger flow carrying capacity of tourist attractions, tourist crowding, overloading, and other problems caused by the frequent occurrence of safety accidents, to the tourist attractions has caused a huge negative impact. For this reason, this paper constructs a model for predicting passenger flows at tourist attractions based on the GWO algorithm. Optimizing the model involves using a differential evolutionary algorithm following a feature study with the Gray Wolf Optimization (GWO) algorithm. Then, for the problems that are prone to occur in the fusion of the GWO algorithm and DE algorithm for solving nonlinear systems of equations, a combined GWO-DE-SVM model is proposed to realize the accurate prediction of tourist attractions’ passenger flow. On this basis, the prediction effect of the GWO-DE-SVM combination model is examined. The loss values of the model in the training set and test set of this paper are around 0.034 and 0.029, respectively, with the lowest average error of 2.524% among all the models. The passenger flow and the total tourism revenue of W tourist attraction in the coming year are successfully predicted in the practical application, which is estimated to be 681 million and 106.88 million yuan, respectively. Million and 106.88 million yuan are the respective amounts. And two peaks of tourist attractions in W were predicted in May and October. This study provides a scientific basis for the management of scenic spots to prevent tourist crowding.

Aiming at the current characteristics of English teaching in colleges and universities which are too theoretical, this paper combines the ideas of functional linguistics to innovate the teaching method, focusing on constructing a personalized English teaching resources recommendation system to assist English teaching. Among them, in the teaching resources recommendation module, the collaborative filtering algorithm is improved by calculating the learner trust degree and similarity degree, then describing the learner features and learning resources features respectively, constructing the multi-objective optimization function, and solving the optimal value by using the hybrid differential evolutionary algorithm. The constructed English teaching resources recommendation system is applied to actual teaching. For the answer situation of knowledge difficulty category 5, the highest level students can still maintain the average correct rate of 55.21%. In the comparison experiment of innovation effect demonstration, the experimental group and the control group reached statistical significance in fluency index (P=0.038

In this paper, firstly, the selection of individuals is made according to the selection probability size, and in order to reduce the greediness of selection, the algorithm only selects the starting vectors of base vectors and difference vectors for random sorting selection, and the rest of vectors are still selected according to multi-objective sorting. Secondly, according to the teaching content of Civic Education, the multimodal corpus content needs to take into account the characteristics of topicality, comprehensiveness, and appropriateness, and the differential evolution algorithm is proposed to construct the Civic Education multimodal corpus. The results show that self-efficacy, implicit education, and daily education can be obtained at the level of 0.01, which indicates a significant positive effect on Civic Education. Self-efficacy, and other are all below the level of 0.01, which indicates a significant negative influence on Civic Education in colleges and universities. The combined described implicit education and other education cooperate with each other in order to achieve the development of modern ideological and political education.

This paper firstly studies the regional economic law under the perspective of blockchain, using the characteristics of blockchain as a regulatory system outside the bottom layer, but also needs to combine the application scenario of supply chain finance and build a financial and economic regulatory system and a heterogeneous multi-chain cooperative regulatory system. Secondly, in response to the problems such as the irrationality of regional economic law, the price discrimination antitrust litigation system is proposed to play the role of economic recovery. Then the digital currency economic law is studied by using an adaptive differential evolutionary algorithm, using the process of crossover, variation, and selection to obtain digital currency behavioral characteristics so as to ensure the digital currency economic visualization analysis and to plan the monetary policy of economic law and analyze the digital currency economic risk. The results show that Bitcoin’s daily average right-tail VaR (94.8%) is the lowest among the listed crypto-digital currencies at 2.58%, while the left-tail mean is the highest among the three crypto-digital currencies, which is consistent with the fact that BTC has been issued for the longest time and is more mature among the crypto-digital currencies with the most dramatic tail risk variation as well as the largest magnitude.

Linear phase shifting transformer is a new type of phase shifting transformer with the advantages of simple winding structure, easy modularity, and the ability to shift phase at any angle. In order to improve the efficiency and power factor of linear phase shift transformer and reduce the weight of the body, based on the equivalent circuit, this paper first, selects the main design parameters of the linear phase shift transformer model as the optimization object, secondly, selects the constraints and objective function applicable to this model, and then, establishes the linear phase shift transformer optimization design model. Finally, a differential evolutionary algorithm with strong global search performance is used to optimize the design parameters. Taking the 1 kW linear phase shifting transformer as an example, the efficiency, power factor, and weight before and after optimization are compared using finite element simulation to verify the effectiveness of the proposed optimization method. The test results of the experimental prototype prove that the proposed method can effectively solve the optimization design problem of linear phase shifting transformers. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Abstract The Optimal Power Flow (OPF) is a primary tool in planning and installing power systems. It attempts to minimize the operating costs associated with generating and transmitting electrical power by modifying control parameters to satisfy environmental, economic, and operational constraints. Implementing an efficient and robust optimization algorithm for the above‐said problem is critical to achieving such a typical objective. Therefore, this paper introduces and evaluates new variants of the Successive History‐based Adaptive Differential Evolutionary (SHADE) algorithm called ESHADE, SHADE‐SFS, and SHADE‐SAP to solve the OPF problems with equality and inequality constraints. Generally, the static penalty approach is widely used for eliminating infeasible solutions discovered during the search phase when searching for feasible solutions. This approach requires the accurate selection of penalty coefficients, accomplished through the trial‐and‐error method. The proposed ESHADE algorithm is formulated using Self‐Adaptive Penalty (SAP) and Superiority of Feasible Solution (SFS) mechanisms to obtain feasible solutions for OPF problems. Two IEEE bus systems are used to demonstrate the effectiveness of the proposed algorithm in handling OPF problems. The fuel cost and active power loss obtained by the proposed algorithm are better than other state‐of‐the‐art algorithms. The results reveal that the proposed framework offers significant advantages over other algorithms.

Coastal grounding electrodes are currently an important means to alleviate land grounding electrode land constraints. In order to better invert the terrestrial geodesic resistivity in the coastal region, this paper proposes a complete set of inversion technology schemes. First, this paper proposes a layered land model for the coastal region, and a composite geodetic model is modeled by the fold junction of the land model and the ocean. Based on this, an adaptive subdivision boundary element method is proposed for solving the composite soil grounding calculation problem, and the accuracy and advantages of the method are demonstrated by examples. Finally, the paper uses the differential evolutionary algorithm to invert the exploration data of the four-point method in the coastal area, and obtains the parameters of the terrestrial layered geodetic model that meet the engineering requirements. The comparison with the grounding software CDEGS illustrates the effectiveness of the method. This paper carries out the research on the modeling and inversion methods of composite layered soil model, combining advanced numerical calculation methods and artificial intelligence algorithms to provide the support of computational tools for coastal resistivity inversion. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have been showed to be highly vulnerable to imperceptible adversarial perturbations. As a complementary type of adversary, patch attacks that introduce perceptible perturbations to the images have attracted the interest of researchers. Existing patch attacks rely on the architecture of the model or the probabilities of predictions and perform poorly in the decision-based setting, which can still construct a perturbation with the minimal information exposed – the top-1 predicted label. In this work, we first explore the decision-based patch attack. To enhance the attack efficiency, we model the patches using paired key-points and use targeted images as the initialization of patches, and parameter optimizations are all performed on the integer domain. Then, we propose a differential evolutionary algorithm named DevoPatch for query-efficient decision-based patch attacks. Experiments demonstrate that DevoPatch outperforms the state-of-the-art black-box patch attacks in terms of patch area and attack success rate within a given query budget on image classification and face verification. Additionally, we conduct the vulnerability evaluation of ViT and MLP on image classification in the decision-based patch attack setting for the first time. Using DevoPatch, we can evaluate the robustness of models to black-box patch attacks. We believe this method could inspire the design and deployment of robust vision models based on various DNN architectures in the future. [ABSTRACT FROM AUTHOR]

The remarkable accomplishments of deep neural networks (DNN) have led to their widespread adoption in various contexts, including safety-critical applications. Many strategies have been implemented to generate adversarial samples using DNN, raising the question of the security of the model. Adding slight magnitude noise to the input samples during training or testing can misguide DNN to produce different results than the actual one. DNNs are sensitive to indiscernible adversarial samples but readily identifiable by them. Currently, gradient-based approaches are used to generate adversarial samples. Gradient-based methods require internal details of the model, such as several parameters, model type, Etc. Usually, these details are practically unavailable, and calculating the gradient for non-differential models is impossible. In this work, we propose a novel DESapsDE framework based on evolutionary algorithms to generate adversarial samples from the probability of labels. We also incorporated the discussion with the various Generative Adversarial Networks (GANs) models, such as ACGAN, DCGAN, and SAGAN. It has been observed that GANs differ from adversarial sample generation methods and can be applied as defense mechanisms. The proposed method reduced model confidence to 13.09% for the ResNet50 model, 30.34% for the WideResNet model, and 23.1% for the DenseNet model, with an FID score of 16.45. The proposed model varies from the GAN model. It applies to attack-on-network models as a preventive major to make the model robust.

The significance of short-term power load forecasting extends to grid dispatching and future planning. To address the issues of nonlinear characteristics and poor prediction accuracy of original power load, a hybrid short-term power load forecasting method was proposed based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Grey Relation Analysis (GRA), Improved Secondary Variation Differential Evolution Algorithm (SVDE), Bidirectional Gated Recurrent Unit (BiGRU) and Error Correction Model. Firstly, ICEEMDAN decomposition is used to divide the sequence into Intrinsic Mode Functions (IMF) and a residual component (Res), and GRA is used to reconstruct the partial component sequences to improve the model operation efficiency and anti-interference ability. Then, an Improved Secondary Variation Differential Evolution Algorithm (SVDE) is proposed to perform hyperparameter optimization on BiGRU neural networks to predict the processed component sequences. Finally, an Error Correction Model based on SVDE-BiGRU is established by the processed mode components and factors such as temperature and holiday weekends to further increase the accuracy of its load prediction. The experimental results show that the RMSE, MSE, and MAPE of the prediction method are 89.72, 60.56, and 0.55% on average, respectively. Compared with the common BiGRU prediction method its MAE value is reduced by 79.02%. Compared with several mainstream methods, its MAE value is reduced by 70.88% at maximum and 40.62% at minimum, which proves the effectiveness and accuracy of the proposed method in short-term power load forecasting.

The Optimal Power Flow (OPF) is a primary tool in planning and installing power systems. It attempts to minimize the operating costs associated with generating and transmitting electrical power by modifying control parameters to satisfy environmental, economic, and operational constraints. Implementing an efficient and robust optimization algorithm for the above‐said problem is critical to achieving such a typical objective. Therefore, this paper introduces and evaluates new variants of the Successive History‐based Adaptive Differential Evolutionary (SHADE) algorithm called ESHADE, SHADE‐SFS, and SHADE‐SAP to solve the OPF problems with equality and inequality constraints. Generally, the static penalty approach is widely used for eliminating infeasible solutions discovered during the search phase when searching for feasible solutions. This approach requires the accurate selection of penalty coefficients, accomplished through the trial‐and‐error method. The proposed ESHADE algorithm is formulated using Self‐Adaptive Penalty (SAP) and Superiority of Feasible Solution (SFS) mechanisms to obtain feasible solutions for OPF problems. Two IEEE bus systems are used to demonstrate the effectiveness of the proposed algorithm in handling OPF problems. The fuel cost and active power loss obtained by the proposed algorithm are better than other state‐of‐the‐art algorithms. The results reveal that the proposed framework offers significant advantages over other algorithms. [ABSTRACT FROM AUTHOR]

Confronting the challenges provided by jumbo container vessels, managers of world-class seaports must take into consideration water depths-related constraints when making the vessel berthing decisions. That is, when the water depths satisfy the requirement of jumbo vessels' drafts, these vessels are allowed to sail through the navigation channel. In this paper, we study a tactical berth allocation problem under the consideration of time-varying water depths. For the resolution, we propose an integer programming model, a set partitioning formulation and an improved differential evolutionary algorithm. Computational experiments demonstrate that the set partitioning formulation outperforms the integer programming model and can be applied to solve small- and medium-sized problems. The improved differential evolutionary algorithm is recommended for large-sized problems. [ABSTRACT FROM AUTHOR]

Using electrically large antennas for array applications may cause grating lobes and this can change the array pattern. To achieve a low side lobe level for boresight pattern for linear or planar array antennas, in the literature it is seen that the common method is the use of feeding each antenna with some amplitude distribution and keeping the distance between the antennas less than a wavelength. However, in this study the distance between the antennas is chosen to be greater than a wavelength. Therefore, the common methods cannot be used. To achieve a low side lob level in the radiation pattern for an array of antennas composed of antennas with a distance greater than one wavelength. The aperture length of each antenna is chosen to be variable and an analytical solution method is used to calculate the amplitude of each aperture field of the horn antennas. Thus, the determination of unit horns' optimal aperture lengths Li can be considered a multivariable optimization problem. This challenging problem was achieved using a novel meta‐heuristic optimization algorithm called the differential evolutionary algorithm (DEA), where the aperture lengths of each array element are taken as inputs to find the optimal side lobe level (SLL). Based on the obtained solutions, a 4 × 1 horn array antenna design alongside its feed network was designed for Ku‐band applications. The design of the array is simulated in CST simulation software, and the obtained simulated results had been compared with experimental results and counterpart designs in the literature. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms are widely used in complex problems that cannot be solved by traditional computing methods due to their powerful optimization capabilities. However, for high-complexity problems, the fitness function evaluation may take hours or even days to complete. The surrogate-assisted meta-heuristic algorithm effectively solves this kind of long solution time for the fitness function. Therefore, this paper proposes an efficient surrogate-assisted hybrid meta-heuristic algorithm by combining the surrogate-assisted model with gannet optimization algorithm (GOA) and the differential evolution (DE) algorithm, abbreviated as SAGD. We explicitly propose a new add-point strategy based on information from historical surrogate models, using information from historical surrogate models to allow the selection of better candidates for the evaluation of true fitness values and the local radial basis function (RBF) surrogate to model the landscape of the objective function. The control strategy selects two efficient meta-heuristic algorithms to predict the training model samples and perform updates. A generation-based optimal restart strategy is also incorporated in SAGD to select suitable samples to restart the meta-heuristic algorithm. We tested the SAGD algorithm using seven commonly used benchmark functions and the wireless sensor network (WSN) coverage problem. The results show that the SAGD algorithm performs well in solving expensive optimization problems. [ABSTRACT FROM AUTHOR]

An improved differential evolutionary cement mill operation index decision algorithm based on constraint control and selection strategy is proposed to address the problem that the operation index is usually decided by manual experience in the cement mill operation process, which causes unqualified cement specific surface area and excessive cement mill power consumption. The algorithm uses an improved differential evolutionary algorithm to solve the long short-term memory network (LSTM) power consumption prediction model and specific surface area prediction model based on PCA and XGBoost. Constraints are set in the solution process to obtain the optimal solution that satisfies the quality index and the power consumption index. The optimal solution is used to guide the scheduling of each piece of equipment in the production process and to make decisions on the operating index of the cement mill grinding process so that the specific surface area is qualified and the power consumption is reduced. [ABSTRACT FROM AUTHOR]

Due to the asynchronous development of observation and transition capabilities, the original image data (OID) obtained by a one-time observation cannot be completely transmitted in one transmission cycle between the EOS and GS , which is named a visible time window (VTW). First, it is needed to segment the OID into several segmented image datasets (SIDs), which are then transmitted in several VTWs. Furthermore, this can cause the satellite image data downlink scheduling problem (SIDSP). This study defines an innovative SIDSP as a satellite image data downlink scheduling problem with the family attribute (SIDSPFA), where massive OID are segmented using a fast segmentation operator, and all SIDs and other no-segmented OID are transmitted in the second step. In addition, two optimization objectives, namely the failure rate (FR) and Segmentation Time (ST) of the image data transmission, are defined to formalize the SIDSPFA as a bi-objective discrete optimization model. Further, a bi-stage differential evolutionary algorithm (DE-CD) is developed. The results of the extensive simulation tests verify the efficiency of the models, strategies, algorithms, and operators used in this study. • Model: A novel satellite image data downlink scheduling model is formatted considering two phases. • Algorithm: A bi-stage differential evolutionary algorithm (DE-CD) is developed. • Benchmark: The enriched simulation experiments is designed. [ABSTRACT FROM AUTHOR]

Energy saving in palletizing robot is a fundamental problem in the field of industrial robots. However, the palletizing robot often suffers from the problems of high energy consumption and lacking flexibility. In this work, we introduce a novel differential evolution algorithm to address the adverse effects caused by the instability of the initial trajectory parameters while reducing the energy. Specially, a simplified analytical model of the palletizing robot is firstly developed. Then, the simplified analytical model and the differential evolutionary algorithm are combined to form a planner with the goal of reducing energy consumption. The energy saving planner optimizes the initial parameters of the trajectories collected by the bionic demonstration system, which in turn enables a reduction in the operating power consumption of the palletizing robot. The major novelty of this article is the use of a differential evolutionary algorithm that can save the energy consumption as well as boosting its flexibility. Comparing with the traditional algorithms, the proposed method can achieve the state-of-the-art performance. Simulated and actual experimental results illustrate that the optimized trajectory parameters can effectively reduce the energy consumption of palletizing robot by 16%. [ABSTRACT FROM AUTHOR]

The time-delay estimation (TDE) method is the primary method for predicting leakage locations in buried water distribution pipelines. The accuracy of TDE depends on the acoustic speed and attenuation of the leakage signal propagating along the pipeline. The analytical prediction model is the typical approach for obtaining the propagation speed and attenuation of leakage waves. However, the embedding parameters of the buried pipe in this model must be measured using soil tests, which are very difficult, costly, and time-consuming. These factors restrict the application of the TDE method in pinpointing pipeline leakage. A method for inverse identification of pipe embedding parameters using discrete wavenumbers obtained in field testing is presented in this paper, and the differential evolution algorithm is introduced as an optimization solution. A field experiment is conducted to validate the method, and the test wavenumbers are measured in a cast-iron pipeline. The estimated sensitive parameters in the analytical model using the method are soil elastic modulus, Poisson's ratio, and pipe–soil contact coefficient, while the conventional soil test is used to measure the soil density due to the character of the optimization algorithm and the soil properties. The application effects show that the estimated parameters are close to those measured from a conventional soil test. The wave speed based on the estimated parameters was an excellent match for the on-site test in the engineering application. This work provides a less costly and more straightforward way to apply the TDE method for leak localization in buried pipelines. [ABSTRACT FROM AUTHOR]

Random speckle structured light can increase the texture information of the object surface, so it is added in the binocular stereo vision system to solve the matching ambiguity problem caused by the surface with repetitive pattern or no texture. To improve the reconstruction quality, many current researches utilize multiple speckle patterns for projection and use stereo matching methods based on spatiotemporal correlation. This paper presents a novel random speckle 3D reconstruction scheme, in which multiple speckle patterns are used and a weighted-fusion-based spatiotemporal matching cost function (STMCF) is proposed to find the corresponding points in speckle stereo image pairs. Furthermore, a parameter optimization method based on differential evolutionary (DE) algorithm is designed for automatically determining the values of all parameters included in STMCF. In this method, since there is no suitable training data with ground truth, we explore a training strategy where a passive stereo vision dataset with ground truth is used as training data and then apply the learned parameter value to the stereo matching of speckle stereo image pairs. Various experimental results verify that our scheme can realize accurate and high-quality 3D reconstruction efficiently and the proposed STMCF exhibits superior performance in terms of accuracy, computation time and reconstruction quality than the state-of-the-art method based on spatiotemporal correlation. [ABSTRACT FROM AUTHOR]

To optimize the energy efficiency of edge computing system with energy harvesting, this paper proposes an energy-efficient task offloading method optimized by differential evolution. First, a wireless edge computing network model is established to analyze the energy harvesting, task offloading and task calculation of the system, as well as the total number of calculated bits and total energy consumption of the system. Second, according to the total number of calculated bits and total energy consumption of the system, an objective function is established to optimize the energy efficiency of system, and a differential evolution based optimization method is proposed, with which the optimal energy efficiency of system calculation, offloading time, calculation time and frequency are obtained. Experimental results show that the proposed method can not only achieve better convergence effect, but also can effectively solve the energy shortage problem of the micro-equipment and extend the service life of the equipment.

As fossil fuel is being depleted, the percentage of wind power capacity in total electricity generation is increasing. In order to improve the absorption capacity of wind power, wind power prediction has been introduced. Aiming at the disadvantage of low prediction accuracy and unstable model of traditional extreme learning machine (ELM), a kernel extreme learning machine based on differential evolution (DE) and cross validation optimization method is proposed to predict short-term wind power generation. Firstly, the average mean square error (MSE) verified by k folding and cross validation is adopted as the error function of the model to improve the stability and generalization performance of the model. Secondly, differential evolution algorithm is used to optimize the regularization coefficient and kernel width of the kernel extreme learning machine with cross validation and improve the precision of model is 8.34%. Finally, compared with the application of extreme learning machine with genetic algorithm and cross validation to a wind farm prediction case in northwest China, the experimental results show that the convergence rate of this method is twice that of genetic algorithm (GA) optimization algorithm, and the accuracy is higher.

Meta-heuristic algorithms are widely used in complex problems that cannot be solved by traditional computing methods due to their powerful optimization capabilities. However, for high-complexity problems, the fitness function evaluation may take hours or even days to complete. The surrogate-assisted meta-heuristic algorithm effectively solves this kind of long solution time for the fitness function. Therefore, this paper proposes an efficient surrogate-assisted hybrid meta-heuristic algorithm by combining the surrogate-assisted model with gannet optimization algorithm (GOA) and the differential evolution (DE) algorithm, abbreviated as SAGD. We explicitly propose a new add-point strategy based on information from historical surrogate models, using information from historical surrogate models to allow the selection of better candidates for the evaluation of true fitness values and the local radial basis function (RBF) surrogate to model the landscape of the objective function. The control strategy selects two efficient meta-heuristic algorithms to predict the training model samples and perform updates. A generation-based optimal restart strategy is also incorporated in SAGD to select suitable samples to restart the meta-heuristic algorithm. We tested the SAGD algorithm using seven commonly used benchmark functions and the wireless sensor network (WSN) coverage problem. The results show that the SAGD algorithm performs well in solving expensive optimization problems.

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The black widow spider optimization algorithm (BWOA) had the problems of slow convergence speed and easily to falling into local optimum mode. To address these problems, this paper proposes a multi-strategy black widow spider optimization algorithm (IBWOA). First, Gauss chaotic mapping is introduced to initialize the population to ensure the diversity of the algorithm at the initial stage. Then, the sine cosine strategy is introduced to perturb the individuals during iteration to improve the global search ability of the algorithm. In addition, the elite opposition-based learning strategy is introduced to improve convergence speed of algorithm. Finally, the mutation method of the differential evolution algorithm is integrated to reorganize the individuals with poor fitness values. Through the analysis of the optimization results of 13 benchmark test functions and a part of CEC2017 test functions, the effectiveness and rationality of each improved strategy are verified. Moreover, it shows that the proposed algorithm has significant improvement in solution accuracy, performance and convergence speed compared with other algorithms. Furthermore, the IBWOA algorithm is used to solve six practical constrained engineering problems. The results show that the IBWOA has excellent optimization ability and scalability.

Energy saving in palletizing robot is a fundamental problem in the field of industrial robots. However, the palletizing robot often suffers from the problems of high energy consumption and lacking flexibility. In this work, we introduce a novel differential evolution algorithm to address the adverse effects caused by the instability of the initial trajectory parameters while reducing the energy. Specially, a simplified analytical model of the palletizing robot is firstly developed. Then, the simplified analytical model and the differential evolutionary algorithm are combined to form a planner with the goal of reducing energy consumption. The energy saving planner optimizes the initial parameters of the trajectories collected by the bionic demonstration system, which in turn enables a reduction in the operating power consumption of the palletizing robot. The major novelty of this article is the use of a differential evolutionary algorithm that can save the energy consumption as well as boosting its flexibility. Comparing with the traditional algorithms, the proposed method can achieve the state-of-the-art performance. Simulated and actual experimental results illustrate that the optimized trajectory parameters can effectively reduce the energy consumption of palletizing robot by 16%.

The Differential Evolutionary algorithm (DE) is a well-known and effective metaheuristic method for continuous optimization problems. However, due to contraction stagnation and early convergence problems, the performance of the DE algorithm can be influenced at the convergence speed and optimization accuracy. Therefore, this paper proposes an improved DE algorithm with the Levy flights mechanism and the Simulated Annealing algorithm (SA), namely DESA-LF algorithm, which effectively uses the advantages of them with generating the better candidate solutions to reduce the convergence speed for making the optimal solution towards the global optimal direction. Finally, we use six typical functions with different complexities and make the comparisons with the DE algorithm, the DE-LF algorithm and other meta-heuristic algorithms to verify the efficient of DESA-LF. From the results and convergence curves, the DESA-LF has the advantages of extending the convergence speed and enhancing the optimization accuracy. Therefore, it demonstrates that convergence speed and optimization accuracy of DESA-LF are highly efficient and superior. [ABSTRACT FROM AUTHOR]

With the increased load of turbine stages, shock waves and secondary flow influence the performance of turbines more and more significantly. Aiming at improving the aerodynamic efficiency of transonic turbines, an integrated aerodynamic optimization platform for turbines has been established. The aerodynamic shape of the flow path and blades is simultaneously parametrized by a directly manipulated free-form deformation (DFFD) method, and the optimization is conducted via a data-driven differential evolutionary algorithm. Furthermore, a prescreening strategy is introduced to enhance the convergence speed of the optimizer. The TTM-stage is selected as the research object. The result shows that the turbine isentropic efficiency at the design point increases from 90.45 % to 91.94 % after optimization. The main reasons for aerodynamic performance enhancement are the weakening of shock waves and the reduction of separation regions. As a comparison, another two optimization cases are carried out. One optimizes the blades individually with the same methods and configurations, and the other optimizes the blades and flow path simultaneously without the prescreening strategy. The optimized isentropic efficiency in the latter two cases is 91.31 % and 90.92 %, whichis 0.63 % and 1.02 % lower than the first optimization result, respectively. This shows the effectiveness of the proposed optimization method for turbine performance improvement. • Highlight 1. The aerodynamic shape of the flow path and blades of turbines is optimized simultaneously. • Highlight 2. DFFD parametrization method is used reduce the control variables and realize flexible deformation for turbine stages. • Highlight 3. Prescreening strategy is embedded into the evolutionary optimizer to reduce number of exactly evaluated samples. [ABSTRACT FROM AUTHOR]

The time-delay estimation (TDE) method is the primary method for predicting leakage locations in buried water distribution pipelines. The accuracy of TDE depends on the acoustic speed and attenuation of the leakage signal propagating along the pipeline. The analytical prediction model is the typical approach for obtaining the propagation speed and attenuation of leakage waves. However, the embedding parameters of the buried pipe in this model must be measured using soil tests, which are very difficult, costly, and time-consuming. These factors restrict the application of the TDE method in pinpointing pipeline leakage. A method for inverse identification of pipe embedding parameters using discrete wavenumbers obtained in field testing is presented in this paper, and the differential evolution algorithm is introduced as an optimization solution. A field experiment is conducted to validate the method, and the test wavenumbers are measured in a cast-iron pipeline. The estimated sensitive parameters in the analytical model using the method are soil elastic modulus, Poisson’s ratio, and pipe–soil contact coefficient, while the conventional soil test is used to measure the soil density due to the character of the optimization algorithm and the soil properties. The application effects show that the estimated parameters are close to those measured from a conventional soil test. The wave speed based on the estimated parameters was an excellent match for the on-site test in the engineering application. This work provides a less costly and more straightforward way to apply the TDE method for leak localization in buried pipelines.

Random speckle structured light can increase the texture information of the object surface, so it is added in the binocular stereo vision system to solve the matching ambiguity problem caused by the surface with repetitive pattern or no texture. To improve the reconstruction quality, many current researches utilize multiple speckle patterns for projection and use stereo matching methods based on spatiotemporal correlation. This paper presents a novel random speckle 3D reconstruction scheme, in which multiple speckle patterns are used and a weighted-fusion-based spatiotemporal matching cost function (STMCF) is proposed to find the corresponding points in speckle stereo image pairs. Furthermore, a parameter optimization method based on differential evolutionary (DE) algorithm is designed for automatically determining the values of all parameters included in STMCF. In this method, since there is no suitable training data with ground truth, we explore a training strategy where a passive stereo vision dataset with ground truth is used as training data and then apply the learned parameter value to the stereo matching of speckle stereo image pairs. Various experimental results verify that our scheme can realize accurate and high-quality 3D reconstruction efficiently and the proposed STMCF exhibits superior performance in terms of accuracy, computation time and reconstruction quality than the state-of-the-art method based on spatiotemporal correlation.

Linear induction motors (LIMs) have been widely used in rail transit. However, Due to the breaking of the primary core and the large air gap, the efficiency and power factor of LIMs are seriously damaged, causing a large amount of energy waste. To improve the efficiency and power factor of LIMs for urban rail transit, we present a new optimization method for the design of a short primary double-sided linear induction motor (SP-DLIM) with a rated speed of 45 km/h and small thrust. The method is based on a steady state equivalent circuit model and the differential evolutionary algorithm (DEA). Moreover, the design constraints and the objective functions are proposed for the optimization problem. Finally, the optimized SP-DLIM is simulated by 2D transient finite element method (FEM). The 2-D transient FEM results verify the accuracy of the optimization method proposed in this paper.

The efficiency and effectiveness of differential evolution (DE) greatly depend on the mutation operator due to the principle that different mutation operators are beneficial to different evolutionary states. However, it is not easy to automatically and effectively identify the evolutionary state. In this paper, we propose an evolutionary state estimation method (ESE) based on the correlation coefficient between the population's distributions in objective space (Δf) and solution space (Δx). To be specific, Δf consists of the distances between each individual and the current best individual based on their objective function values, while Δx includes the Euclidean distances between each individual and the current best individual based on their positions in the search space. Based on the correlation coefficient between Δx and Δf, the entire evolutionary process is classified into three kinds of state. At each generation, the evolutionary state is firstly determined according to the correlation coefficient, subsequently adaptively choosing a mutation operator from the corresponding candidate operator pool for each individual to generate its mutation vector. Moreover, a two-level selection mechanism (TLSM) is presented to get away from stagnation. The algorithm combines DE with ESE and TLSM (DEET for short) is proposed. Experimental results on twenty frequently used benchmark functions and the CEC2017 test problems show that DEET exhibits very competitive performance compared with other state-of-the-art DE variants. [ABSTRACT FROM AUTHOR]

Bilevel problems are widely used to describe the decision problems with hierarchical upper–lower-level structures in many economic fields. The bilevel optimisation problem (BLOP) is intrinsically NP-hard when its objectives and constraints are complex and the decision variables are large in scale at both levels. An efficient hybrid differential evolutionary algorithm for BLOP (HDEAB) is proposed where the optimal lower level value function mapping method, the differential evolutionary algorithm, k-nearest neighbours (KNN) and a nested local search are hybridised to improve the computational accuracy and efficiency. To show the performance of the HDEAB, numerical studies were conducted on SMD (Sinha, Maro and Deb) instances and an application example of optimising a venture capital staged-financing contract. The results demonstrate that the HDEAB outperforms the BLEAQ (bilevel evolutionary algorithm based on quadratic approximations) greatly in solving the BLOPs with different scales. [ABSTRACT FROM AUTHOR]