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Remote Sensing Image Object Detection (RSIOD) faces the challenges of multi-scale objects, dense overlap of objects and uneven data distribution in practical applications. In order to solve these problems, this paper proposes a YOLO-ACPHD RSIOD algorithm. The algorithm adopts Adaptive Condition Awareness Technology (ACAT), which can dynamically adjust the parameters of the convolution kernel, so as to adapt to the objects of different scales and positions. Compared with the traditional fixed convolution kernel, this dynamic adjustment can better adapt to the diversity of scale, direction and shape of the object, thus improving the accuracy and robustness of Object Detection (OD). In addition, a High-Dimensional Decoupling Technology (HDDT) is used to reduce the amount of calculation to 1/N by performing deep convolution on the input data and then performing spatial convolution on each channel. When dealing with large-scale Remote Sensing Image (RSI) data, this reduction in computation can significantly improve the efficiency of the algorithm and accelerate the speed of OD, so as to better adapt to the needs of practical application scenarios. Through the experimental verification of the RSOD RSI data set, the YOLO-ACPHD model in this paper shows very satisfactory performance. The F1 value reaches 0.99, the Precision value reaches 1, the Precision-Recall value reaches 0.994, the Recall value reaches 1, and the mAP value reaches 99.36 % , which indicates that the model shows the highest level in the accuracy and comprehensiveness of OD. [ABSTRACT FROM AUTHOR]

Classic multidimensional scaling (MDS) and scaling by majorizing a complex function (SMACOF) are well-known centralized algorithms that are used to solve MDS problem. In this paper, we present a distributed algorithm for solving MDS problem. Estimations of coordinates are performed concurrently under the assumption that each item knows only its own position and its distances from its neighbors and their approximated present locations. The update process is done by calculating the average of the current coordinate of each object and its projections on the solution spaces allocated to it by its neighbors. We apply the method to the problem of sensor localization and obtain numerical results that demonstrate the efficacy of our suggested strategy. [ABSTRACT FROM AUTHOR]

In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites. [ABSTRACT FROM AUTHOR]

We study a class of chance constrained programs (CCPs) where the underlying distribution is modeled by a Gaussian mixture model. As the original work, Hu et al. (IISE Trans 54(12):1117–1130, 2022. https://doi.org/10.1080/24725854.2021.2001608) developed a spatial branch-and-bound (B &B) algorithm to solve the problems. In this paper, we propose an enhanced procedure to speed up the computation of B &B algorithm. We design an enhanced pruning strategy that explores high-potential domains and an augmented branching strategy that prevents redundant computations. We integrate the new strategies into original framework to develop an enhanced B &B algorithm, and illustrate how the enhanced algorithm improves on the original approach. Furthermore, we extend the enhanced B &B framework to handle the CCPs with multiple chance constraints, which is not considered in the previous work. We evaluate the performance of our new algorithm through extensive numerical experiments and apply it to solve a real-world portfolio selection problem. [ABSTRACT FROM AUTHOR]

The sparse triangular solver (SpTRSV) is mostly used for scientific and engineering applications. The structured-grid triangular solver of regular dependencies (STRSV) is a special kind of SpTRSV. Some general SpTRSVs that disregards the regularity of the matrix are unsuitable for solving this problem. This paper proposes an efficient parallel algorithm for STRSV on the SW26010 (a kind of China independently designed many-core processors), namely swStructTRSV. The algorithm makes full use of the fine-grained and low latency communication characteristics of the SW26010 to reduce the waiting time for synchronization, maximizes the regularity of access to improve memory access bandwidth, and achieves overlap between memory access and computation simultaneously. Moreover, the idea of the algorithm can be extended to incomplete LU factorization (ILU factorization) because of consistent dependencies. The experimental results on a core group(8 * 8 network composed of 64 cores) of SW26010 show that swStructTRSV can achieve an average speedup of over 30 in the sequential version. swStructTRSV on SW26010 achieves solving speedups of 2.2 and 6.3 over the fast STRSV (fSpTRSV) previously implemented on SW26010 and MKL on Intel Xeon Gold 6132, respectively. swStructTRSV significantly outperforms cuSparse on NVIDIA TITAN RTX in terms of overall execution time. [ABSTRACT FROM AUTHOR]

The Manta Ray Foraging Optimization Algorithm (MRFO) is a metaheuristic algorithm for solving real-world problems. However, MRFO suffers from slow convergence precision and is easily trapped in a local optimal. Hence, to overcome these deficiencies, this paper proposes an Improved MRFO algorithm (IMRFO) that employs Tent chaotic mapping, the bidirectional search strategy, and the Levy flight strategy. Among these strategies, Tent chaotic mapping distributes the manta ray more uniformly and improves the quality of the initial solution, while the bidirectional search strategy expands the search area. The Levy flight strategy strengthens the algorithm's ability to escape from local optimal. To verify IMRFO's performance, the algorithm is compared with 10 other algorithms on 23 benchmark functions, the CEC2017 and CEC2022 benchmark suites, and five engineering problems, with statistical analysis illustrating the superiority and significance of the difference between IMRFO and other algorithms. The results indicate that the IMRFO outperforms the competitor optimization algorithms. [ABSTRACT FROM AUTHOR]

The optimal operation of reservoir groups is a strongly constrained, multi-stage, and high-dimensional optimization problem. In response to this issue, this article couples the standard Pelican optimization algorithm with adaptive ε constraint methods, and further improves the optimization performance of the algorithm by initializing the population with a good point set, reverse differential evolution, and optimal individual t-distribution perturbation strategy. Based on this, an improved Pelican algorithm coupled with adaptive ε constraint method is proposed (ε-IPOA). The performance of the algorithm was tested through 24 constraint testing functions to find the optimal ability and solve constraint optimization problems. The results showed that the algorithm has strong optimization ability and stable performance. In this paper, we select Sanmenxia and Xiaolangdi reservoirs as the research objects, establish the maximum peak-cutting model of terrace reservoirs, apply the ε-IPOA algorithm to solve the model, and compare it with the ε-POA (Pelican algorithm coupled with adaptive ε constraint method) and ε-DE (Differential Evolution Algorithm) algorithms, the results indicate that ε. The peak flow rate of the Huayuankou control point solved by the IPOA algorithm is 12,319 m3/s, which is much lower than the safe overflow flow rate of 22,000 m3/s at the Huayuankou control point, with a peak shaving rate of 44%, and other algorithms do not find effective solutions meeting the constraint conditions. This paper provides a new idea for solving the problem of flood control optimal operation of cascade reservoirs. [ABSTRACT FROM AUTHOR]

The optimal operation of reservoir groups is a strongly constrained, multi-stage, and high-dimensional optimization problem. In response to this issue, this article couples the standard Pelican optimization algorithm with adaptive ε constraint methods, and further improves the optimization performance of the algorithm by initializing the population with a good point set, reverse differential evolution, and optimal individual t-distribution perturbation strategy. Based on this, an improved Pelican algorithm coupled with adaptive ε constraint method is proposed (ε-IPOA). The performance of the algorithm was tested through 24 constraint testing functions to find the optimal ability and solve constraint optimization problems. The results showed that the algorithm has strong optimization ability and stable performance. In this paper, we select Sanmenxia and Xiaolangdi reservoirs as the research objects, establish the maximum peak-cutting model of terrace reservoirs, apply the ε-IPOA algorithm to solve the model, and compare it with the ε-POA (Pelican algorithm coupled with adaptive ε constraint method) and ε-DE (Differential Evolution Algorithm) algorithms, the results indicate that ε. The peak flow rate of the Huayuankou control point solved by the IPOA algorithm is 12,319 m3/s, which is much lower than the safe overflow flow rate of 22,000 m3/s at the Huayuankou control point, with a peak shaving rate of 44%, and other algorithms do not find effective solutions meeting the constraint conditions. This paper provides a new idea for solving the problem of flood control optimal operation of cascade reservoirs. [ABSTRACT FROM AUTHOR]

Identifying the location of the sensor nodes is a challenging aspect of Wireless Sensor Networks (WSNs) in a distributed environment. It is vital to understand where data and events come from when sensor nodes collect data and report on them. This paper uses a nature-inspired Jaya algorithm to estimate the location coordinates of target nodes in a distributed architecture of WSNs. The Jaya algorithm handles the nonlinear optimization problem of node localization in a randomly distributed sensor node configuration. The range-based, Received Signal Strength Indicator method is used for the distance estimation. MATLAB software is used to assess the proposed work, and a comparison is made with the Particle Swarm Optimization, Krill Herd Optimization, and Salp Swarm Algorithm based node localization algorithms in terms of localization error and computation time. The localization error analysis is done for the different numbers of anchor nodes and different values of degree of irregularity to verify the effectiveness of the proposed work. [ABSTRACT FROM AUTHOR]

This paper reports of theoretical and computational results related to an original concept of consensus clustering involving what we call the projective distance between partitions. This distance is defined as the squared difference between a partition incidence matrix and its image over the orthogonal projection in the linear space spanning the other partition incidence matrix. It appears, provided that the ensemble clustering is of a sufficient size, agglomerate clustering with the semi-average within-cluster similarity criterion effectively solves the problem of consensus partition and, moreover, of the number of clusters in it. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms, widely applied across various domains due to their simplicity and strong optimization capabilities, play a crucial role in problem-solving. While the Aquila Optimizer is recognized for its effectiveness, it often exhibits slow convergence rates and susceptibility to local optima in certain scenarios. To address these concerns, this paper introduces an enhanced version, termed Tent-enhanced Aquila Optimizer (TEAO). TEAO incorporates the Tent chaotic map to initialize the Aquila population, promoting a more uniform distribution within the solution space. To balance exploration and exploitation, novel formulas are proposed, accelerating convergence while ensuring precision. The effectiveness of the TEAO algorithm is validated through a comprehensive comparison with 14 state-of-the-art algorithms using 23 classical benchmark test functions. Additionally, to assess the practical feasibility of the approach, TEAO is applied to six constrained engineering problems and benchmarked against the performance of the same 14 algorithms. All experimental results consistently demonstrate that TEAO outperforms other advanced algorithms in terms of solution quality and stability, establishing it as a more competitive choice for optimization tasks. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles are useful tools for many applications. However, autonomous path planning for Unmanned Aerial Vehicles in unfamiliar environments is a challenging problem when facing a series of problems such as poor consistency, high influence by the native controller of the Unmanned Aerial Vehicles. In this paper, we investigate reinforcement learning-based autonomous local path planning methods for Unmanned Aerial Vehicles with high autonomous decision-making capability and locally high portability. We propose an autonomous local path planning algorithm based on the TD3 strategy to solve the problem of local obstacle avoidance and path planning in unfamiliar environments using autonomous decision-making of Unmanned Aerial Vehicles. The simulation results on Gazebo show that our method can effectively realize the autonomous local path planning task for Unmanned Aerial Vehicles, the success rate of path planning with our method can reach 93% under the interference of no obstacles, and 92% in the environment with obstacles. Finally, our method can be used for autonomous path planning of Unmanned Aerial Vehicles in unfamiliar environments. [ABSTRACT FROM AUTHOR]

In order to improve tracking accuracy of time-varying sparse signals, a sparse state Kalman filter algorithm based on Kalman gain matrix is proposed in this paper. Under the constraint of sparse state, minimizing the mean square error and solving the optimization problem by using the symmetrical alternating direction multiplier method (symmetrical ADMM), while keeping the update expression of state estimation unchanged, a Kalman gain matrix is obtained which can make the state estimation sparse. In addition, this paper also discusses two different sparse constraints which are L1 norm constraint and cardinality function constraint. The proposed algorithm can be implemented under the framework of conventional Kalman filter algorithm, no need to introduce additional frameworks. Two groups of dynamic signal models, a slow change of signal and a random walk of nonzero elements, are simulated. Simulation results show that the proposed algorithm can improve the tracking accuracy of conventional Kalman filter algorithm for time-varying sparse signal. [ABSTRACT FROM AUTHOR]

In this paper the step by step descent algorithm is extended to continuous optimization problems and applied to the optimal sizing of truss structures. The step by step descent method, recently developed, was applied to the discrete optimization of rigid structures. To solve continuous problems, a multi-stage optimization algorithm is proposed, which involves implementing the step by step descent method in multiple stages. In each stage, the continuous areas of the variables are progressively divided into a finite number of points. The algorithm is based on the search for the steepest gradient descent direction that minimizes the cost function and employs a wise heuristic approach to bypassing local optima. To evaluate the algorithm's performance, a study was carried out on ten mathematical and four truss optimization problems. For unconstrained mathematical problems, the algorithm demonstrates quick converges to optimum solution from any starting point. For truss structures, the starting design point is selected based on structural knowledge to enhance convergence speed. The obtained results surpass those achieved by other state-of-the-art optimization algorithms, affirming the efficiency, robustness and the promptness of the proposed method. [ABSTRACT FROM AUTHOR]

Products with complex free-form surfaces, such as aerospace impellers and automotive engines, are typically manufactured with ball-end milling. The machined surface topography is critical to these key components' performance in harsh environments. Accurate prediction of the machined surface topography is essential to obtaining the optimized process parameters to achieve the best possible surface quality. An improved Z-MAP method based on the sequential quadratic programming (SQP) algorithm is proposed for efficient ball-end milling surface topography simulation in this paper. Compared to the traditional Z-MAP method, this improved method does not discretize the cutter edge and does not require a strict time-step limit. At each simulation time interval, the workpiece surface grid points falling into the cutting-edge instantaneous sweep polygon are searched to reduce the computation scale. An optimization problem, which combines these grid points' coordinates with the cutting-edge trajectory model, is established for the accurate cutting time and cutting-edge element. The SQP algorithm is used to solve this optimization problem due to its global search capability. The surface topography is then updated by comparing the height of the cutting-edge element with the corresponding grid point. Experimental results indicate that the simulated surface topography is consistent with the measured surface topography. The proposed method achieves the same level of accuracy as the traditional method while requiring less computing time. The proposed method for fast surface topography in ball-end milling shows potential in optimizing process parameters to guarantee the surface quality of critical components. [ABSTRACT FROM AUTHOR]

The results of a parallel implementation of a randomized vector algorithm for solving systems of linear equations are presented in the paper. The solution is represented in the form of a Neumann series. The stochastic method computes this series by sampling only random columns, avoiding multiplication of matrix by matrix and matrix by vector. We consider the case when the matrix is too large to fit in random-access memory (RAM). We use two approaches to solve this problem. In the first approach, the matrix is divided into parts that are distributed among MPI processes and stored in the available RAM of the cluster nodes. In the second approach, the entire matrix is stored on each node's hard drive, loaded into RAM, and processed in parts. Independent Monte Carlo experiments for random column indices are distributed among MPI processes or OpenMP threads for both approaches to matrix storage. The efficiency of parallel implementations is analyzed. Results are given for a system governed by dense matrices of size 10 4 and 10 5 . [ABSTRACT FROM AUTHOR]

Fault diagnosis of interconnection networks is vital to ensuring the reliability and maintenance of multiprocessor systems. Based on graph theory, diagnostic algorithms for solving the problem of fault diagnosis in interconnection networks have been widely studied. As the number of processors in multiprocessor systems has increased in recent years, fault diagnosis algorithms based on graph theory cannot meet the current diagnosis requirements of some interconnection networks, such as t-diagnosable systems. In this paper, we use the evolution diagnosis approach to study fault diagnosis in t-diagnosable systems under the PMC model. First, for a t-diagnosable system G with a syndrome σ , we use a simple centralized algorithm to generate its simplified diagnosis graph G f , which contains all the faulty nodes in G. Based on the graph, we prove that the faulty node set in G is the minimum cover set of G f . Next, we prove that the problem of computing the minimum cover set for G f is equivalent to the problem of computing the optimal solution of a zero-one integer program. Using this result, we propose a novel genetic algorithm to solve the problem of the zero-one integer program. The simulation results show that the diagnostic accuracy of our proposed algorithm is equal to or greater than 96% and that the proposed algorithm outperforms its competitors in terms of diagnostic accuracy, number of iterations and running time. [ABSTRACT FROM AUTHOR]

In the field of intelligent manufacturing, robot grasping and sorting is important content. However, there are some disadvantages in the traditional single-view-based manipulator grasping methods by using a 2D camera, where the efficiency and the accuracy of grasping are both low when facing the scene of stacking and occlusion for the reason that there is information missing by single-view 2D camera-based methods while acquiring scene information, and the methods of grasping only can't change the difficult-to-grasp scene which is stack and occluded. Regarding the issue above, a pushing-grasping collaborative method based on the deep Q-network in dual viewpoints is proposed in this paper. This method in this paper adopts an improved deep Q-network algorithm, with an RGB-D camera to obtain the information of objects' RGB images and point clouds from two viewpoints, which solved the problem of lack of information missing. What's more, it combines the pushing and grasping actions with the deep Q-network, which make it have the ability of active exploration, so that the trained manipulator can make the scenes less stacking and occlusion, and with the help of that, it can perform well in more complicated grasping scenes. In addition, we improved the reward function of the deep Q-network and propose the piecewise reward function to speed up the convergence of the deep Q-network. We trained different models and tried different methods in the V-REP simulation environment, and it drew a conclusion that the method proposed in this paper converges quickly and the success rate of grasping objects in unstructured scenes raises up to 83.5%. Besides, it shows the generalization ability and well performance when novel objects appear in the scenes that the manipulator has never grasped before. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new inertial iterative method to solve classical variational inequalities with pseudomonotone and Lipschitz continuous operators in the setting of a real Hilbert space. The proposed iterative scheme is basically analogous to the extragradient method used to solve the problems of variational inequalities in real Hilbert spaces. The strong convergence of the proposed algorithm is set up with the prior knowledge of Lipschitz's constant of an operator. Finally, several computational experiments are listed to show the applicability and efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Harris hawks optimization (HHO) is a new meta-heuristic algorithm that builds a model by imitating the predation process of Harris hawks. In order to solve the problems of poor convergence speed caused by uniform choice position update formula in the exploration stage of basic HHO and falling into local optimization caused by insufficient population richness in the later stage of the algorithm, a Harris hawks optimization based on global cross-variation and tent mapping (CRTHHO) is proposed in this paper. Firstly, the tent mapping is introduced in the exploration stage to optimize random parameter q to speed up the convergence in the early stage. Secondly, the crossover mutation operator is introduced to cross and mutate the global optimal position in each iteration process. The greedy strategy is used to select, which prevents the algorithm from falling into local optimal because of skipping the optimal solution and improves the convergence accuracy of the algorithm. In order to investigate the performance of CRTHHO, experiments are carried out on ten benchmark functions and the CEC2017 test set. Experimental results show that the CRTHHO algorithm performs better than the HHO algorithm and is competitive with five advanced meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a novel parallel algorithm is proposed to solve the problems of heavy computation and long simulation time in the field of compressible flows. In this algorithm, a third-order upwind scheme and a fourth-order central difference scheme are employed, with a third-order Runge-Kutta method for time stepping. Considering the powerful floating-point computing ability of the Graphics Processing Unit (GPU), this paper establishes the algorithm on the basis of GPU. Moreover, the direct numerical simulation method is adopted in this algorithm to improve the solution accuracy of the simulation results. To further enhance the efficiency of the algorithm, several optimization strategies are explored in the design of the algorithm as well. Both accuracy and feasibility of the algorithm are verified by a classical two-dimensional example. Compared with solving this example on the Central Processing Unit platform, the experimental results demonstrate that the maximum speedup ratio achieved by our approach is 18.03 times. [ABSTRACT FROM AUTHOR]

In complex networks, key nodes are important factors that directly affect network structure and functions. Therefore, accurate mining and identification of key nodes are crucial to achieving better control and a higher utilization rate of complex networks. To address this problem, this paper proposes an accurate and efficient algorithm for critical node mining. The influential nodes are determined using both global and local information (GLI) to solve the shortcoming of the existing key node identification methods that consider either local or global information. The proposed method considers two main factors, global and local influences. The global influence is determined using the K-shell hierarchical information of a node, and local influence is obtained considering the number of edges connected by the node and the given values of adjacent nodes. The given values of adjacent nodes are determined based on the degree and K-shell hierarchical information. Further, the similarity coefficient of neighbors is considered, which enhances the differentiation degree of the adjacent given values. The proposed method solves the problems of the high complexity of global information-based algorithms and the low accuracy of local information-based algorithms. The proposed method is verified by simulation experiments using the SIR and SI models as a reference, and twelve typical real-world networks are used for the comparison. The proposed GLI algorithm is compared with several common algorithms at different periods. The comparison results show that the GLI algorithm can effectively explore influential nodes in complex networks. [ABSTRACT FROM AUTHOR]

The paper describes an algorithm of calculating the parameters of four maneuvers that allows transferring an active spacecraft to a given vicinity of a space object (SO). The motion occurs in the vicinity of a circular orbit. There are two steps that constitute the process of maneuvering parameters determination: the enumeration of possible angles of impulses application in the allowed maneuvering intervals and the determination of velocity components for each combination of these angles. For the determination of velocity components, two systems of four and two linear equations are solved. The solution of problem represents the angles of impulse applications and the corresponding velocity components that provide the transferring with minimal delta-v. An algorithm for dropout of angles combination leading to obviously non-optimal solutions is proposed. By using this algorithm one can significantly reduce the time for solving the problem. The main feature of the proposed method of maneuver parameter determination is the possibility of finding a solution with the accountancy of the maximum possible errors of the velocity impulse magnitudes arising from their execution. [ABSTRACT FROM AUTHOR]

In this paper, a new stochastic optimization algorithm is introduced, called Driving Training-Based Optimization (DTBO), which mimics the human activity of driving training. The fundamental inspiration behind the DTBO design is the learning process to drive in the driving school and the training of the driving instructor. DTBO is mathematically modeled in three phases: (1) training by the driving instructor, (2) patterning of students from instructor skills, and (3) practice. The performance of DTBO in optimization is evaluated on a set of 53 standard objective functions of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and IEEE CEC2017 test functions types. The optimization results show that DTBO has been able to provide appropriate solutions to optimization problems by maintaining a proper balance between exploration and exploitation. The performance quality of DTBO is compared with the results of 11 well-known algorithms. The simulation results show that DTBO performs better compared to 11 competitor algorithms and is more efficient in optimization applications. [ABSTRACT FROM AUTHOR]

Timing determinism in embedded real-time systems requires meeting timing constraints not only for individual tasks but also for chains of tasks that involve multiple messages. End-to-end analysis is a commonly used approach for solving such problems. However, the temporal properties of tasks often have uncertainty, which makes end-to-end analysis challenging and prone to errors. In this paper, we focus on enhancing the precision and safety of end-to-end timing analysis by introducing a novel probabilistic method. Our approach involves establishing a probabilistic model for end-to-end timing analysis and implementing two algorithms: the maximum data age detection algorithm and the end-to-end timing deadline miss probability detection algorithm. The experimental results indicate that our approach surpasses traditional analytical methods in terms of safety and significantly enhances the capability to detect the probability of missing end-to-end deadlines. [ABSTRACT FROM AUTHOR]

In this paper, we present an integer programming formulation for the windy profitable location rural postman problem (WPLRPP). We state and prove a theorem concerning the dimension of the associated polyhedron. Then, we use this theorem to study some trivial facets of the presented polyhedron. Furthermore, we adapt and validate several large families of valid inequalities for the WPLRPP. We also develop an efficient branch-and-cut algorithm for solving large WPLRPP instances, which leverages those inequalities. We compare our presented branch-and-cut algorithm with other efficient algorithms in the literature. The numerical results show that our algorithm solves larger problem instances and requires considerably less computing time than other algorithms in the literature. [ABSTRACT FROM AUTHOR]

The Jacobi iterative algorithm has the characteristic of low computational load, and multiple components of the solution can be solved independently. This paper applies these characteristics to the ternary optical computer, which can be used for parallel optimization because it has a large number of data bits and reconfigurable processor bits. Therefore, a new parallel design scheme is constructed to solve the problem of slow efficiency in solving large linear equations. And the elaborate experiment is used to verify. The experimental method is to simulate the calculation on the ternary optical computer experimental platform. Then, the resource consumption is numerically calculated and summarized to measure the feasibility of the parallel design. Eventually, the results show that the parallel design has obvious advantages in computing speed. The Jacobi iterative algorithm is optimized in parallel on ternary optical processor for the first time. There are two parallel highlights of the scheme. First, the n components are calculated in full parallel. Second, the modified signed-digit (MSD) multiplier based on the minimum module and one-step MSD adder are used to calculate each component to eliminate the impact of large amount of data on calculation time. The research provides a new method for fast solution of large linear equations. [ABSTRACT FROM AUTHOR]

The problem of learning automata from example traces (but no equivalence or membership queries) is fundamental in automata learning theory and practice. In this paper, we study this problem for finite-state machines with inputs and outputs, and in particular for Moore machines. We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input–output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging. We prove that MooreMI has the fundamental identification in the limit property. We compare the algorithms experimentally in terms of the size of the learned machine and several notions of accuracy, introduced in this paper. We also carry out a performance comparison against two existing tools (LearnLib and flexfringe). Finally, we compare with OSTIA, an algorithm that learns a more general class of transducers and find that OSTIA generally does not learn a Moore machine, even when fed with a characteristic sample. [ABSTRACT FROM AUTHOR]

In this research, an agro-supply chain in the context of both economic and environmental issues has been investigated. To this end, a bi-objective model is formulated as a mixed-integer linear programming that aims to minimize the total costs and CO2 emissions. It generates the integration between purchasing, transporting, and storing decisions, considering specific characteristics of agro-products such as seasonality, perishability, and uncertainty. This study provides a different set of temperature conditions for preserving products from spoilage. In addition, a robust optimization approach is used to tackle the uncertainty in this paper. Then, ε -constraint method is used to convert the bi-objective model to a single one. To solve the problem, Lagrangian relaxation algorithm is applied as an efficient approach giving lower bounds for the original problem and used for estimating upper bounds. At the end, a real case study is presented to give valuable insight via assessing the impacts of uncertainty in system costs. [ABSTRACT FROM AUTHOR]

Bridging the different parts together is considered a simple but effective strategy to reduce the number of piercing operations during laser cutting. However, fast bridging is never an easy task. In this paper, we present a near-linear bridging algorithm for the input parts with the shortest total bridge length. At first, the input part contours are discretized into a point cloud, then the point cloud is triangulated with the Delaunay standard. The shortest line segments between any two adjacent parts are found in the triangles connecting the two parts. These segments are finally extended into bridges. To solve the problem of the damages to the contour characteristics caused by the bridges, some restrictions are set on the screening of the discrete point cloud and the Delaunay triangles. This algorithm not only ensures the minimum total distance of all bridges, but also avoids the problem of generating bridge loops. Computational experiments show that the proposed bridging algorithm is much faster than that in existing commercial software. The feasibility and superiority of the algorithm are verified by actual lasering cutting experiments. [ABSTRACT FROM AUTHOR]

In this paper, a new stochastic optimization algorithm is introduced, called Driving Training-Based Optimization (DTBO), which mimics the human activity of driving training. The fundamental inspiration behind the DTBO design is the learning process to drive in the driving school and the training of the driving instructor. DTBO is mathematically modeled in three phases: (1) training by the driving instructor, (2) patterning of students from instructor skills, and (3) practice. The performance of DTBO in optimization is evaluated on a set of 53 standard objective functions of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and IEEE CEC2017 test functions types. The optimization results show that DTBO has been able to provide appropriate solutions to optimization problems by maintaining a proper balance between exploration and exploitation. The performance quality of DTBO is compared with the results of 11 well-known algorithms. The simulation results show that DTBO performs better compared to 11 competitor algorithms and is more efficient in optimization applications. [ABSTRACT FROM AUTHOR]

The metaheuristic optimization algorithms are relatively new optimization algorithms introduced to solve optimization problems in recent years. For example, the firefly algorithm (FA) is one of the metaheuristic algorithms inspired by the fireflies' flashing behavior. However, its weakness in terms of exploration and early convergence has been pointed out. In this paper, two approaches were proposed to improve the FA. In the first proposed approach, a new improved opposition-based learning FA (IOFA) method was presented to accelerate the convergence and improve the FA's exploration capability. In the second proposed approach, a symbiotic organisms search (SOS) algorithm improved the exploration and exploitation of the first approach; two new parameters set these two goals, and the second approach was named IOFASOS. The purpose of the second method is that in the process of the SOS algorithm, the whole population is effective in the IOFA method to find solutions in the early stages of implementation, and with each iteration, fewer solutions are affected in the population. The experiments on 24 standard benchmark functions were conducted, and the first proposed approach showed a better performance in the small and medium dimensions and exhibited a relatively moderate performance in the higher dimensions. In contrast, the second proposed approach was better in increasing dimensions. In general, the empirical results showed that the two new approaches outperform other algorithms in most mathematical benchmarking functions. Thus, The IOFASOS model has more efficient solutions. [ABSTRACT FROM AUTHOR]

With low hardware cost and power consumption, direction-of-arrival (DOA) estimation exploiting one-bit quantized array data has become an attractive topic. In this paper, the gridless compressed sensing (CS) with multiple measurement vectors for one-bit DOA estimation is investigated. First, the one-bit quantized signal model is established. Then an atomic norm minimization scheme is proposed based on the output of one-bit quantizer, in which the objective function is reformulated as a semidefinite programming problem combined with a one-sided l 1 -norm constraint, representing the sign inconsistency between the quantized and unquantized measurements. To efficiently solve such a problem and reduce its computational complexity, an accelerated proximal gradient-based algorithm is developed. The proposed approach outperforms the one-bit MUSIC in a small number of measurements, and avoids the grid-mismatch issue of several existing one-bit CS methods. Numerical experiments are conducted to validate the superiorities of the proposed one-bit DOA estimation approach in accuracy and running time. [ABSTRACT FROM AUTHOR]

The introduction of cloud computing has brought about significant developments in information technology. Users can benefit from the multitude of cloud technology services only by connecting to the internet. In cloud computing, load balancing is the fundamental issue that has challenged experts in this research area. Load balancing helps increase user satisfaction and enhance systems' productivity through efficient and fair work assignments between computing resources. Besides, maintaining a load balancing among resources would be difficult because the resources are usually distributed in a heterogeneous way. Many load-balancing methods try to solve this problem by the metaheuristics algorithm, and each of them attempted to enhance the operation and efficiency of systems. In this paper, Grey wolf optimization (GWO) algorithm has been used based on the resource reliability capability to maintain proper load balancing. In this method, first, the GWO algorithm tries to find the unemployed or busy nodes and, after discovering this node, try to calculate each node's threshold and fitness function. The results of simulation in CloudSim showed that the costs and response time in the proposed method are less than the other methods, and the obtained solutions are ideal. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a novel operation to reconstruction tetrahedrons within a certain region, which we call MFRC (Multi-face reconstruction). During the existing tetrahedral mesh improvement methods, the flip operation is one of the very important components. However, due to the limited area affected by the flip, the improvement of the mesh quality by the flip operation is also very limited. The proposed MFRC algorithm solves this problem. MFRC can reconstruct the local mesh in a larger range and can find the optimal tetrahedron division in the target area within acceptable time complexity. Therefore, based on the MFRC algorithm, we combined other operations including smoothing, edge removal, face removal, and vertex insertion/deletion to develop an effective mesh quality improvement method. Numerical experiments of dozens of meshes show that the algorithm can effectively improve the low-quality elements in the tetrahedral mesh, and can effectively reduce the running time, which has important significance for the quality improvement of large-scale mesh. [ABSTRACT FROM AUTHOR]

With the rapid development of computer hardware in the past three decades, various classic algorithms such as neural computing and bionic optimization computing have been widely used in practical problems. This paper extended the new bionic algorithm-flock algorithm proposed in 2014 and obtained a multi-objective flock algorithm to solve the multi-objective problem. This study used aggregate functions to define social ranks, and simulated the foraging behavior of chickens in the process of searching for food in the objective space and found the balance between diversity and convergence when looking for the best Pareto solution. The algorithm took five types of bi-objective functions and four types of three-objective functions as objects and compared it with four more widely used algorithms in multi-objective problems. The results demonstrate that the MOCSO (multi-objective chicken swarm optimization) algorithm shows better results in the optimization of multi-objective problems. [ABSTRACT FROM AUTHOR]

Predicting beneficial and valuable miRNA–disease associations (MDAs) by doing biological laboratory experiments is costly and time-consuming. Proposing a forceful and meaningful computational method for predicting MDAs is essential and captivated many computer scientists in recent years. In this paper, we proposed a new computational method to predict miRNA–disease associations using improved random walk with restart and integrating multiple similarities (RWRMMDA). We used a WKNKN algorithm as a pre-processing step to solve the problem of sparsity and incompletion of data to reduce the negative impact of a large number of missing associations. Two heterogeneous networks in disease and miRNA spaces were built by integrating multiple similarity networks, respectively, and different walk probabilities could be designated to each linked neighbor node of the disease or miRNA node in line with its degree in respective networks. Finally, an improve extended random walk with restart algorithm based on miRNA similarity-based and disease similarity-based heterogeneous networks was used to calculate miRNA–disease association prediction probabilities. The experiments showed that our proposed method achieved a momentous performance with Global LOOCV AUC (Area Under Roc Curve) and AUPR (Area Under Precision-Recall Curve) values of 0.9882 and 0.9066, respectively. And the best AUC and AUPR values under fivefold cross-validation of 0.9855 and 0.8642 which are proven by statistical tests, respectively. In comparison with other previous related methods, it outperformed than NTSHMDA, PMFMDA, IMCMDA and MCLPMDA methods in both AUC and AUPR values. In case studies of Breast Neoplasms, Carcinoma Hepatocellular and Stomach Neoplasms diseases, it inferred 1, 12 and 7 new associations out of top 40 predicted associated miRNAs for each disease, respectively. All of these new inferred associations have been confirmed in different databases or literatures. [ABSTRACT FROM AUTHOR]

Simultaneous optimization of several competing objectives requires increasing the capability of optimization algorithms. This paper proposes the multi-objective moth swarm algorithm, for the first time, to solve various multi-objective problems. In the proposed algorithm, a new definition for pathfinder moths and moonlight was proposed to enhance the synchronization capability as well as to maintain a good spread of non-dominated solutions. In addition, the crowding-distance mechanism was employed to select the most efficient solutions within the population. This mechanism indicates the distribution of non-dominated solutions around a particular non-dominated solution. Accordingly, a set of non-dominated solutions obtained by the proposed multi-objective algorithm is kept in an archive to be used later for improving its exploratory capability. The capability of the proposed MOMSA was investigated by a set of multi-objective benchmark problems having 7 to 30 dimensions. The results were compared with three well-known meta-heuristics of multi-objective evolutionary algorithm based on decomposition (MOEA/D), Pareto envelope-based selection algorithm II (PESA-II), and multi-objective ant lion optimizer (MOALO). Four metrics of generational distance (GD), spacing (S), spread (Δ), and maximum spread (MS) were employed for comparison purposes. The qualitative and quantitative results indicated the superior performance and the higher capability of the proposed MOMSA algorithm over the other algorithms. The MOMSA algorithm with the average values of CPU time = 2771 s, GD = 0.138, S = 0.063, Δ = 1.053, and MS = 0.878 proved to be a robust and reliable model for multi-objective optimization. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Things, mobile edge computing which provides physical resources closer to end users has gained considerable popularity in academic and industrial field. As the number of edge server increases, accessing effective edge services fast is an urgent problem to be solved. In this paper, we mainly focus on the cold-start problem for service recommendation based on location of users and services. Address this conundrum, we propose a service recommendation method based on collaborative filtering (CF) and location, by comprehensively considering the characteristic of services at the edge, mobility and demands of users at different time periods. In detail, we synthesize the service characteristics of each dimension in different time slices through multidimensional weighting method at first. Then We further introduce the idea of Inverse CF Rec to the traditional CF and predict the lost quality of service (QoS) to solve the problem of sparse data. Finally, a recommendation algorithm based on predicted QoS and user geographic location is proposed to recommend appropriate services to users. The experimental results show that our multidimensional inverse similarity recommendation algorithm based on time-aware collaborative filtering (MDITCF) outperforms Inverse CF Rec in terms of the accuracy of recommendation. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation has paramount importance in array signal processing tasks. A practical issue regarding DOA estimation is the uncertainty in gain/phase of some of the sensor elements. In this paper, we focus on the 2-dimensional DOA (2D-DOA) estimation in the presence of gain/phase uncertainty. We propose a grid-based modeling for the 2D-DOA estimation of the uniform rectangular array considering the array gain/phase uncertainty. In order to solve the modeled problem, we suggest an algorithm to exploit the joint sparse properties of the problem. Furthermore, we have used a zooming-based technique to estimate the 2D-DOAs with much lower computational complexity. The efficiency of the proposed method has been validated through various simulation scenarios which confirm the superiority of the suggested algorithm over its counterparts. [ABSTRACT FROM AUTHOR]

Under the framework of compressed sensing theory, the greedy algorithm achieves good reconstruction performance with known signal sparsity. However, unknown sparsity of sparse signals in practical applications brings obstacles for signal reconstruction. Specifically, the conventional sparsity adaptive adjustment algorithm takes long time to finish the reconstruction, and the accuracy of reconstruction is not good enough. To solve this problem, this paper proposes a new matching pursuit reconstruction algorithm based on bidirectional sparsity adaptive adjustment and weak selection of atoms (BSA-WSAMP). In this algorithm, the optimization strategy for atom weak selection is employed to update the support set, and the idea of "zoom" bidirectional variable step-size is applied to achieve the sparsity adaptive adjustment. Based on this, the number of iterations can be reduced effectively, and the accurate reconstruction of the sparse signal is obtained. Simulation results indicate that the proposed BSA-WSAMP algorithm achieves better adaptive characteristic of the sparsity, higher reconstruction quality, lower reconstruction complexity, and less reconstruction time than some existing reconstruction algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we extend techniques developed in the context of the Travelling Salesperson Problem for cycle problems. Particularly, we study the shrinking of support graphs and the exact algorithms for subcycle elimination separation problems. The efficient application of the considered techniques has proved to be essential in the Travelling Salesperson Problem when solving large size problems by Branch-and-Cut, and this has been the motivation behind this work. Regarding the shrinking of support graphs, we prove the validity of the Padberg–Rinaldi general shrinking rules and the Crowder–Padberg subcycle-safe shrinking rules. Concerning the subcycle separation problems, we extend two exact separation algorithms, the Dynamic Hong and the Extended Padberg–Grötschel algorithms, which are shown to be superior to the ones used so far in the literature of cycle problems. The proposed techniques are empirically tested in 24 subcycle elimination problem instances generated by solving the Orienteering Problem (involving up to 15,112 vertices) with Branch-and-Cut. The experiments suggest the relevance of the proposed techniques for cycle problems. The obtained average speedup for the subcycle separation problems in the Orienteering Problem when the proposed techniques are used together is around 50 times in medium-sized instances and around 250 times in large-sized instances. [ABSTRACT FROM AUTHOR]

We propose a dead-end control algorithm for the exact solution of NP-hard combinatorial optimization problems. The efficiency of the algorithm is demonstrated by examples of solving the set-partition and 0-1 knapsack problems. The paper also shows that the use of the idea of dead-end controls when implementing the dynamic programming method can considerably reduce the number of problem state variables at each optimization step. A comparative analysis of the proposed method with known algorithms for solving these problems is carried out. [ABSTRACT FROM AUTHOR]

Feature selection is one of the main steps in preprocessing data in machine learning, and its goal is to reduce features by removing additional and noisy features. Feature selection methods and feature reduction in a dataset must consider the accuracy of the classifying algorithms. Meta-heuristic algorithms serve as the most successful and promising methods to solve this problem. Symbiotic Organisms Search (SOS) is one of the most successful meta-heuristic algorithms inspired by organisms' interaction in nature called mutualism, commensalism, and parasitism. In this paper, three SOS-based binary approaches are offered to solve the feature selection problem. In the first and second approaches, several S-shaped transfer functions and several Chaotic Tent Function-based V-shaped transfer functions called BSOSST and BSOSVT are used to make the binary SOS (BSOS). In the third approach, an advanced BSOS based on changing SOS and the chaotic Tent function operators called EBCSOS is provided. The EBCSOS algorithm uses the chaotic Tent function and the Gaussian mutation to increase usefulness and exploration. Moreover, two new operators, i.e., BMPT and BCPT, are suggested to make the commensalism and mutualism stage binary based on a chaotic function to solve the feature selection problem. Finally, the proposed BSOSST and BSOSVT methods and the advanced version of EBCSOS were implemented on 25 datasets than the basic algorithm's binary meta-heuristic algorithms. Various experiments demonstrated that the proposed EBCSOS algorithm outperformed other methods in terms of several features and accuracy. To further confirm the proposed EBCSOS algorithm, the problem of detecting spam E-mails was applied, with the results of this experiment indicating that the proposed EBCSOS algorithm significantly improved the accuracy and speed of all categories in detecting spam E-mails. [ABSTRACT FROM AUTHOR]

Surface comparison and matching is a challenging problem in computer vision. While elastic Riemannian metrics provide meaningful shape distances and point correspondences via the geodesic boundary value problem, solving this problem numerically tends to be difficult. Square root normal fields considerably simplify the computation of certain distances between parametrized surfaces. Yet they leave open the issue of finding optimal reparametrizations, which induce corresponding distances between unparametrized surfaces. This issue has concentrated much effort in recent years and led to the development of several numerical frameworks. In this paper, we take an alternative approach which bypasses the direct estimation of reparametrizations: we relax the geodesic boundary constraint using an auxiliary parametrization-blind varifold fidelity metric. This reformulation has several notable benefits. By avoiding altogether the need for reparametrizations, it provides the flexibility to deal with simplicial meshes of arbitrary topologies and sampling patterns. Moreover, the problem lends itself to a coarse-to-fine multi-resolution implementation, which makes the algorithm scalable to large meshes. Furthermore, this approach extends readily to higher-order feature maps such as square root curvature fields and is also able to include surface textures in the matching problem. We demonstrate these advantages on several examples, synthetic and real. [ABSTRACT FROM AUTHOR]

We suggest in this paper a parallel implementation of cellular automation and global random walk algorithms for solving drift–diffusion–recombination problems which in contrast to the classical random walk on spheres (RWS) methods calculate the solution in any desired family of m prescribed points. The method uses only N trajectories in contrast to mN trajectories in the conventional RWS algorithm. The idea is based on the adjoint symmetry property of the Green function and a double randomization approach. The synchronous multi-particle cellular automaton model of drift–diffusion–recombination is based on known cellular automata. The global RWS and cellular automaton models are tested against the exact solutions of the equation. The accuracy and computer time of both algorithms is analyzed. Parallel codes for the Monte Carlo and cellular automaton algorithms are implemented. The domain decomposition method is employed for the cellular automaton parallel implementation. The global random walk algorithm is parallelized by the Monte Carlo trajectories distribution among the cluster cores. The efficiency of the parallel codes is studied. [ABSTRACT FROM AUTHOR]

We consider a class of hyperbolic systems of linear inhomogeneous partial differential equations with one spatial variable. As a rule, in the case of systems of partial differential equations, when solving particular problems, additional conditions are immediately used to ensure the uniqueness of the solution. However, this greatly complicates the construction of a solution in the case of additional conditions of nonstandard form. For a similar situation in the case of ordinary differential equations, one tries to find a general solution, for which one can then try to use the given additional conditions. However, for systems of partial differential equations, this approach is difficult, since, as a rule, in this case it is not possible to construct the general solution. For the class of systems of linear inhomogeneous partial differential equations considered in the present paper, it was possible to find an algorithm for constructing a general solution. A distinctive feature of the considered systems of equations is the multiplicity of the corresponding characteristics. As an application of the proposed algorithm, a general solution of the Kolmogorov system of equations for the probabilities of the states of a process is obtained, which describes the behavior of a popular model of a stochastic system of the type -out-of- with a general distribution of the repair time for failing components. The indicated system of Kolmogorov equations is a system of partial differential equations of the mentioned class. Therefore, it is possible to construct a general solution for this system. [ABSTRACT FROM AUTHOR]

Definition of an optimal path in the real-world routing problems is not necessarily the shortest one, because parameters such as travel time, safety, quality, and smoothness also played essential roles in the definition of optimality. In this paper, we use bi-colored graphs for modeling urban and heterogeneous environments and introduce variations of constraint routing problems. Bi-colored graphs are a kind of directed graphs whose vertices are divided into two subsets of white and gray. We consider two criteria, minimizing the length and minimizing the number of gray vertices and present two problems called gray vertices bounded shortest path problem and length bounded shortest path problem on bi-colored graphs. We propose an efficient time label-setting algorithm to solve these problems. Likewise, we simulate the algorithm and compare it with the related path planning methods on random graphs as well as real-world environments. The simulation results show the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a normalized subband spline adaptive filter (SAF-NSAF) algorithm to solve the problem that linear subband adaptive filtering cannot identify nonlinear systems. The weight update of the proposed algorithm is conducted using the principle of minimum disturbance. Since a delayless structure is used in the proposed algorithm, a delay is not introduced into the update process. The effectiveness of the proposed algorithm is verified by simulations. Also, the mean and mean square stability of the proposed algorithm are evaluated using the principle of conservation of energy. The simulation results demonstrate that the performance of the proposed algorithm outperforms other cited nonlinear algorithms. [ABSTRACT FROM AUTHOR]

The usefulness of machine learning algorithms has led to their widespread adoption prior to the development of a conceptual framework for making sense of them. One common response to this situation is to say that machine learning suffers from a "black box problem." That is, machine learning algorithms are "opaque" to human users, failing to be "interpretable" or "explicable" in terms that would render categorization procedures "understandable." The purpose of this paper is to challenge the widespread agreement about the existence and importance of a black box problem. The first section argues that "interpretability" and cognates lack precise meanings when applied to algorithms. This makes the concepts difficult to use when trying to solve the problems that have motivated the call for interpretability (etc.). Furthermore, since there is no adequate account of the concepts themselves, it is not possible to assess whether particular technical features supply formal definitions of those concepts. The second section argues that there are ways of being a responsible user of these algorithms that do not require interpretability (etc.). In many cases in which a black box problem is cited, interpretability is a means to a further end such as justification or non-discrimination. Since addressing these problems need not involve something that looks like an "interpretation" (etc.) of an algorithm, the focus on interpretability artificially constrains the solution space by characterizing one possible solution as the problem itself. Where possible, discussion should be reformulated in terms of the ends of interpretability. [ABSTRACT FROM AUTHOR]