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The current popular video object segmentation algorithms based on memory network indiscriminately update the frame information to the memory pool, fails to make reasonable use of the historical frame information, causing frame information redundancy in the memory pool, resulting in the increase of the computation amount. At the same time, the mask refinement method is relatively rough, resulting in blurred edges of the generated mask. To solve these problems, This paper proposes a video object segmentation algorithm based on dynamic perception update and feature fusion. In order to reasonably utilize the historical frame information, a dynamic perception update module is proposed to selectively update the segmentation frame mask. Meanwhile, a mask refinement module is established to enhance the detail information of the shallow features of the backbone network. This module uses a double kernels fusion block to fuse the different scale information of the features, and finally uses the Laplacian operator to sharpen the edges of the mask. The experimental results show that on the public datasets DAVIS2016, DAVIS2017 and YouTube-VOS 18 , the comprehensive performance of the algorithm in this paper reaches 86.9%, 79.3% and 71.6%, respectively, and the segmentation speed reaches 15FPS on the DAVIS2016 dataset. Compared with many mainstream algorithms in recent years, it has obvious advantages in performance. • Introduce the idea of key frames into video object segmentation and propose a dynamic perception update module. By calculating the IoU changes between consecutive frames, we determine whether to update the current frame information to the memory pool, thereby reducing the redundancy of frame information in the memory pool. • In order to make the final mask more refined, this paper proposes a mask refinement module, which uses channel attention to adaptively weight the target channels of shallow features, the dual-core fusion block is used to expand the fusion feature receptive field, and using edge detection operators to enhance the edge information of features. • Compared with the current state-of-the-art video object segmentation methods, the algorithm in this paper has obvious advantages on multiple public datasets, which proves the superiority of the algorithm in this paper. [ABSTRACT FROM AUTHOR]

This paper is concerned with a variant of the traveling salesman problem (TSP) called the maximum scatter traveling salesman problem (MSTSP). The goal of MSTSP is to find a Hamiltonian cycle that maximizes the minimum length edge of the cycle. This problem has real-world applications in domains such as manufacturing and medical imaging. Both symmetric and asymmetric versions of the problem are considered in this paper. To address this problem, we have developed two evolutionary approaches. Our first approach is based on a genetic algorithm, whereas the second approach is based on a differential evolution algorithm. Initial solution generation procedure, variation operators (crossover and mutation) used in our approaches are designed considering the characteristics of this problem. The solutions obtained through variation operators are improved further through a local search that is specially adapted for MSTSP. For benchmarking, we have compared the performance of our proposed approaches with the state-of-the-art approaches available in the literature. Our approaches obtained better quality solutions in a shorter time for both symmetric and asymmetric versions of the problem, thereby clearly demonstrating their effectiveness in solving MSTSP. • This paper is concerned with the maximum scatter traveling salesman problem (MSTSP). • Two evolutionary approaches are proposed for solving this problem. • First approach is a steady-state genetic algorithm, while the latter is a discrete differential evolution algorithm. • Various components of our approaches are designed considering the characteristics of MSTSP. • Computational results show the effectiveness of our proposed approaches. [ABSTRACT FROM AUTHOR]

This paper considers the offline assignment of fixed priorities in partitioned preemptive real-time systems where tasks have precedence constraints. This problem is crucial in this type of systems, as having a good fixed priority assignment allows for an efficient use of the processing resources while meeting all the deadlines. In the literature, we can find several proposals to solve this problem, which offer varying trade-offs between the quality of their results and their computational complexities. In this paper , we propose a new approach, leveraging existing algorithms that are widely exploited in the field of Machine Learning: Gradient Descent, the Adam Optimizer, and Gradient Noise. We show how to adapt these algorithms to the problem of fixed priority assignment in conjunction with existing worst-case response time analyses. We demonstrate the performance of our proposal on synthetic task-sets with different sizes. This evaluation shows that our proposal is able to find more schedulable solutions than previous heuristics, approximating optimal but intractable algorithms such as MILP or brute-force, while requiring reasonable execution times. [ABSTRACT FROM AUTHOR]

The efficient processing of mass stream data has attracted wide attention in the database field. The skyline query on the sensor data stream can monitor multiple targets in real time, to avoid abnormal events such as fire and explosion, which is very useful in the practical application of sensor data monitoring. However, real-world stream data may often contain incomplete data attributes due to faulty sensing devices or imperfect data collection techniques. Skyline queries over incomplete data streams may lead to a lack of transitivity and loop domination issues. To solve the problem of the skyline query over incomplete data streams, firstly, this paper uses differential dependency rule (DD) to fill the missing attribute values of data in the incomplete data stream. Then, the hierarchical grid index (HGrid) is introduced into the field of skyline query to improve pruning efficiency. In the process of skyline calculation, this paper only keeps as few calculation results as possible for the data that may affect the result to avoid a large number of repeated calculations. Thus, S_IDS (Skyline query algorithm over Incomplete Data Stream) is proposed to query skyline results with high confidence from the incomplete data stream. Finally, by comparing with the most advanced skyline query algorithms over incomplete data streams, the correctness and efficiency of the proposed S_IDS algorithm are proved. [ABSTRACT FROM AUTHOR]

The Barzilai–Borwein (BB) method is a popular and efficient gradient method for solving large-scale unconstrained optimization problems. In general, it converges much faster than the steepest descent (Cauchy) method. However, it may not converge, even when the objective function is strongly convex. To overcome this, by virtue of bounding the distance between sequential iterates, [Burdakov et al. (2019)] proposed a stabilized BB method. In this paper, by combining the stabilized BB method with a projection approach, we develop a stabilized BB projection (SBBP) method to solve nonlinear monotone equations with convex constraints. SBBP does not involve computing matrices proving the usefulness for solving large-scale problems. Its global convergence and Q-linear convergence rate are also established. We report numerical experiments on recovering sparse signals and restoring blurred images arising from compressive sensing, demonstrating the usefulness of SBBP. [ABSTRACT FROM AUTHOR]

Unconstrained quadratic optimization problems are a common mathematical challenge encountered in various domains. These problems involve optimizing quadratic functions without explicit constraints. In a distributed computing environment, solving these optimization problems collectively among multiple computational nodes is a complex and crucial task. This paper introduces a distributed algorithm within a multi-agent framework that aims to find the global minimizer for such problems. The proposed algorithm demonstrates exponential convergence, assuming a static and connected communication network. Additionally, numerical simulations are conducted to support the theoretical findings. • A distributed algorithm is devised to solve the convex quadratic optimization problem. • The algorithm leverages the concept of strong duality in its development. • The technique solves QP over undirected multi-agent networks without exchanging minimizer estimates. • The algorithm achieves rapid, exponential convergence towards the global optimizer. • Simulation studies confirm the theoretical claims. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of dispatching multiple automated guided vehicles (AGVs) in an actual production workshop, aiming to minimize the transportation cost. To solve this problem, a self-adaptive memetic algorithm with Q-learning (Q-SAMA) is proposed. An improved nearest-neighbor task division heuristic is used for generating premium solutions. Additionally, a Q-learning is integrated to select appropriate neighborhood operators, thereby enhancing the algorithm's exploration ability. To prevent the algorithm from falling into a local optimum, the restart strategy is offered. In order to adapt Q-SAMA to different stages in the search process, the traditional crossover and mutation probabilities are no longer used. Instead, a self-adaptive probability is obtained based on the population's degree of concentration, and the sparsity relationship among individuals' fitness. Finally, experimental results validate the effectiveness of the proposed method. It is able to yield better results compared with other five state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we address the prize-collecting generalized minimum spanning tree problem (PC-GMSTP) which aims to find a minimum spanning tree to connect a network of clusters using exactly one vertex per cluster, minimizing the total cost of connecting the clusters while considering both the costs of edges and the prizes offered by the vertices. An Adaptive Multi-meme Memetic Algorithm (AMMA) is proposed to tackle PC-GMSTP, which combines an adaptive reproduction procedure and a collaborated local search procedure. The adaptive reproduction procedure uses either crossover or mutation to produce offspring to maintain a good balance between exploration and exploitation of the search space, and the probability to use crossover or mutation is adaptively adjusted based on the diversity of population. The collaborated local search procedure, which includes two efficient local search operators, can effectively enhance the intensification ability of AMMA due to their complementary features. Extensive computational experiments on 126 challenging instances demonstrate the superiority of AMMA, outperforming 23 best-known solutions from existing literature while achieving similar solutions for the remaining 103 instances. Wilcoxon's signed rank test confirms that the performance of AMMA is significantly better than the state-of-the-art algorithms. • We focus in the prize-collecting generalized minimum spanning tree problem. • We adjust the pre-processing method originally used for GMSTP to tackle PC-GMSTP. • We describe an adaptive multi-meme memetic algorithm for solving this problem. • Our method achieves 23 new best-known solutions out of 126 instances. • Extensive computational results are reported and discussed. [ABSTRACT FROM AUTHOR]

Adversarial sample generation problem is a hot issue in the security field of deep learning. Evolutionary algorithm has been widely used to solve this problem in recent years because of its good global search ability. However, existing methods still suffer from the "curse of dimensionality" when attacking high-resolution images. In this paper, a two-stage frequency domain generation algorithm of black-box adversarial samples based on differential evolution is proposed. In the first stage, a representative image-guided differential evolution method is proposed to quickly generate a universal adversarial perturbation with a high attack success rate in the frequency-domain. In the second stage, a space reduction strategy based on frequency-domain pixel blocks is designed to reduce the search space and alleviate the problem of "curse of dimensionality". In addition, a new space–frequency interaction sensitivity measure is introduced to evaluate the similarity between the adversarial samples and the original images. The adversarial perturbations obtained by the measure are more in line with the subjective perception of the human eye. Finally, compared with several typical black-box adversarial sample generation algorithms, experimental results show that the proposed algorithm can achieve higher attack success rate with less prediction times. • Proposing a two-stage evolutionary generation framework of adversarial samples. • Developing a space–frequency interaction sensitivity measure. • Presenting a representative image-guided differential evolutionary algorithm. • Designing an individual adversarial perturbation generation algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a class of single-allocation hub location problems is investigated from the perspective of upgrading. The latter is understood as an improvement of a set of edges to increase their individual performance, e.g., a decreased unit transportation cost. The goal is to obtain an improved optimal solution to the problem compared to that obtained if upgrading was not done. A budget constraint is assumed to limit the upgrading operations. A flow-based formulation is initially proposed that extends a classical model for uncapacitated single-allocation hub location with complete hub networks. Nevertheless, the fact that the unit costs after upgrading may violate the triangle inequality needs to be accounted for. Since the proposed formulation has a high computing burden, different possibilities are discussed for enhancing it. This leads to devising an efficient branch-and-cut algorithm with different variants. Additionally, a formulation based on the discrete ordered median function is also introduced that is also enhanced and embedded into a branch-and-cut algorithm again with several variants. All models and algorithms are also adapted to problems embedding hub network design decisions. Extensive computational tests were conducted to assess the methodological contributions proposed. • Hub location problems from the perspective of upgrading are analyzed. • Two flow-based formulations to solve the problem are introduced. • The ordered median function is considered to formulate the problem. • The models are adapted embedding hub network design decisions. • Different cuts are included in a branch-and-cut procedure to enhance the performance. [ABSTRACT FROM AUTHOR]

• The spatial-spectral tensor for each pixel in a given HSI is generated by combining spectral vectors of the pixel and its neighboring pixels to express its spatial and spectral information, which, instead of spectral vectors of the pixel, is used as basic processing unit for the HSI classification. • The sparse tensors taken as classification features for pixels are obtained directly from the spatial-spectral tensors by dimension reduction in the tensor framework. At the same time of dimension reduction, more features beneficial to hyperspectral image classification can be extracted. • The STM classifier is deigned to perform the classification of the HSI based on the tensor space containing the obtained sparse tensors. The classification is completed within the tensor framework, effectively ensuring that the structural features of sparse tensors are not destroyed. Spectral vector based Hyperspectral Image (HSI) classification algorithms are difficult to effectively and completely utilize the rich spatial and spectral information contained in HSI, leading to the limited classification accuracy. This paper presents a sparse tensor based Support Tensor Machine (STM) HSI classification algorithm to solve this problem. Firstly, the spatial-spectral tensor for each pixel in a given HSI is generated by combining spectral vectors of the pixel and its neighboring pixels to express its spatial and spectral information, which is used as basic processing unit for HSI classification. Then, the sparse tensors taken as classification features for pixels are obtained directly from the spatial-spectral tensors by dimension reduction in the tensor framework. Finally, the STM classifier is designed to distinguish sparse tensors, effectively preserving the structural information of the tensor. Experiments with three real HSIs are conducted to validate the effectiveness of the Tensor Sparse Representation based STM (TSR-STM) algorithm. The experimental results show that the TSR-STM algorithm achieves better performance compared to three other classification algorithms. [ABSTRACT FROM AUTHOR]

Ship pipe route design (SPRD) is a difficult and time-consuming job due to the complexity of the routing space and the large number of pipes. This paper introduces a hybrid optimization algorithm called SQNSGA-II based on the proposed Square strategy and NSGA-II to efficiently solve SPRD problems in complex situations. The details of the hybrid algorithm are introduced, including the principle of the Square strategy, genetic operators and pipe repair operators et al. Additionally, a new method called Intercept Search strategy is proposed for optimizing branch pipe design in complex scenarios. Finally, we conducted three cases of simulation experiments to demonstrate that the proposed algorithm and strategy can efficiently and accurately optimize the design of pipes, highlighting their pipe routing capabilities in complex engineering projects. • A hybrid algorithm based on a novel deterministic algorithm and NSGA-II. • A novel strategy that can route branch pipes in complex scenarios. • The algorithm and strategy perform well while maintaining high stability. • The algorithm and strategy can efficiently route pipes in complex scenarios. [ABSTRACT FROM AUTHOR]

This paper addresses the two-dimensional strip-packing (2DSP) problem of placing a set of rectangular pieces onto a fixed-width rectangular sheet to minimize the total length used. We propose a Block-Based Heuristic Search Algorithm (BBHSA) to solve 2DSP problems with guillotine cut constraints. Initially, it converts the 2DSP problem into a series of 2D rectangular packing problems (2DRP), where the size of the sheet is fixed. In the BBHSA, rectangular pieces are aggregated into blocks, which are partial solutions without residual space. These blocks provide the ingredients for a good layout and are basic components in a tree-based constructive search process. Two basic operations, called placing & splitting and approximate binary search are used in the search process. Furthermore, several block-based placement rules are explored to speed up the search process and improve solution quality. To verify the performance of our proposed algorithm, we conducted extensive experiments using the zero-waste benchmark and non-zero-waste benchmark instances. The results show that BBHSA demonstrates computational effectiveness, particularly in zero-waste cases, achieving optimal solutions for almost all zero-waste benchmark instances reported in the existing literature. [ABSTRACT FROM AUTHOR]

The feature subset obtained by traditional feature selection algorithms usually contains many irrelevant features and redundant features, which increases the size of the feature set and reduces classification accuracy. More importantly, these irrelevant features and redundant features may lead to local optimum. How to efficiently select the global optimal feature subset from high-dimensional data is a challenging task. To solve the problem, an adaptive dual-strategy constrained optimization-based coevolutionary optimizer for high-dimensional feature selection (ADSCO) is proposed in this paper. The contributions of this algorithm are as follows. Firstly, an adaptive dual strategy constrained optimization mechanism (ADSC) is developed. In the ADSC, the adaptive linear constraint strategy and the random constraint strategy are designed to search for the feature subsets that satisfy the constraints. This can reduce the search space of candidate solutions. Secondly, the dual population-constrained coevolutionary optimizer (CO) is developed to help the algorithm jump out of the local optimum and search for better optimal solutions to further improve the classification accuracy of ADSCO. Finally, the ablation experiments are executed to validate the effectiveness of the proposed ADSC and CO. The ADSCO algorithm is tested on 8 high-dimensional datasets and 8 low-dimensional datasets, and compared with seven typical population feature selection algorithms. Experimental results show that the proposed ADSCO algorithm is superior to the 7 comparison algorithms in terms of classification accuracy and feature subset size, demonstrating its strong competitiveness in tackling high-dimensional data feature selection. [ABSTRACT FROM AUTHOR]

It is well known that multi-UAV cooperative dynamic target path planning is a challenging field. In this field, multi-UAV cooperative dynamic target path planning is very important to achieve efficient task completion. However, the existing algorithms have some limitations in solving the problems of insufficient search range, premature convergence, local optimization and insufficient population diversity. In order to solve these problems and improve the efficiency and accuracy of path planning, this paper proposes an innovative method. Firstly, we use A* algorithm to obtain the initial assignment result of UAV target, and then use Hungarian algorithm and genetic algorithm to optimize the assignment result and expand the target assignment range. Secondly, in the path planning stage, the bat algorithm is improved, and sine function, dynamic expansion factor and nonlinear function are introduced to solve the problems of insufficient search range, premature convergence and local optimization. At the same time, genetic algorithm is used to solve the problem of insufficient population. By optimizing target assignment and path planning, the performance of multi-UAV cooperative system is improved, so as to better adapt to the task requirements in dynamic environment and provide more reliable solutions for UAV applications. The experimental results show that the tracking and path planning are more effective than BA, CCWOA and OUMPOA under the condition of good stability. Compared with BA, the fitness function and convergence speed are improved by 34.64% and 30.29% respectively. [ABSTRACT FROM AUTHOR]

We address in this paper the two-machine job shop scheduling problem with a single server that sets up the jobs before they get processed on the machines. The server is only needed during the set-up and becomes free at the end of this phase. Moreover, the set-ups are non-anticipatory and the set-up times are sequence-independent. We seek a schedule that minimizes the overall completion time, also called the makespan. We propose several lower bounds to the problem and prove the N P -hardness in the strong sense of two restricted cases. In addition, we present a linear time algorithm for a special case. In order to solve the general problem, we develop a genetic and simulated annealing algorithms that use feasibility guaranteed procedures. An experimental study is carried out to analyze the performance of these meta-heuristic methods. [ABSTRACT FROM AUTHOR]

For solving the problem of the lack of full control over the identity data of network users, we propose an identity management method based on the certificateless encryption algorithm of block chain. We also propose a certificateless encryption algorithm combined with state secret algorithm SM2 to reduce the computational cost of the algorithm by using the decentralized advantages of block chain and the characteristics of data tamper-proof. The distributed user identity management mechanism is designed. The algorithm models of user identity registration, identity certificate issuance and identity authentication are optimized to improve the security of identity data management. The model is tested and verified. The results show that the model has fast information response ability, and the average delay is about 0. 4 s. Compared with other algorithms, the scheme in this paper has the highest computational efficiency. Under the same conditions, the computational time is about 6.9% of Su et al. (2020), 63.4% of Xu et al. (2021), 7.0% of Zhang et al. (2023) , and 76.9% of Cheng (2021). The computational performance is greatly improved. [ABSTRACT FROM AUTHOR]

The Multi-Objective Discrete Network Design Problem (MO-DNDP) describes the optimisation of a transport network across multiple metrics. Due to the impractical processing times of exact solutions, heuristics have been harnessed to solve this problem. However, the scale of the networks, the set of candidate links and the metrics assessed have been limited. This research proposes a MO-DNDP that encapsulates 8 metrics across three real networks used as benchmarks scaling from 17 km2 to 41,850 km2 to encapsulate the complexity of optimising transport networks at different scales, traffic demands and constraint densities. To solve this more comprehensive MO-DNDP, this paper proposes the Physarum-inspired algorithm PANDA that improves upon the classical Physarum Solver algorithm by harnessing the attractant sensing and merging capabilities of the biological Physarum Polycephalum slime mould. PANDA is a first attempt at solving the complex MO-DNDP using the Physarum foraging model. PANDA consistently scored better than the classical Solver in the land use rating by circumventing landforms, built-up areas, and environmentally sensitive regions. Additionally, PANDA constructed networks that were on average 30 % better than the existing benchmark networks and 32 % better than the Physarum Solver networks. PANDA improved upon the benchmarks by constructing lower capacity roads that were optimised for the actual traffic demand between each origin–destination pair instead of shared arterials. [ABSTRACT FROM AUTHOR]

Industrial scheduling problems (ISPs), especially industrial production workshop scheduling problems (IPWSPs) in various sectors like manufacturing, and power require allocating resources to tasks within multiple constraints. Addressing these complex challenges involves computationally solving problems with numerous variables and constraints. Lagrangian relaxation-based algorithms (LRAs) effectively tackle ISPs (particularly IPWSPs) by relaxing constraints through multipliers. While LRAs may violate original constraints, heuristic algorithms rectify this by crafting feasible solutions. LRAs offer efficient problem-solving and yield high-quality results with manageable computational effort. This review presents the cutting-edge LRAs for ISPs, based on 200 papers from the fields of supply chain, manufacture, power, etc. It exemplifies LRAs' principles through practical cases, and provides a classification of problems in detail, along with LRA's problem-solving process. We discuss strengths, weaknesses, and future research prospects, offering comprehensive guidance. Summarizing these findings, the review enhances understanding of LRAs' application potential in solving ISPs for practitioners and researchers. • Propose tutorials & examples for mastering ISP solving with LRAs. • Demonstrate an algorithm framework and diverse solution designs. • List pros & cons of using LRAs and analyze applicable scenarios. • Organize LRA development stages and future research directions. [ABSTRACT FROM AUTHOR]

This paper considers the problem of online optimization where the objective function is time-varying. In particular, we extend coordinate descent type algorithms to the online case, where the objective function varies after a finite number of iterations of the algorithm. Instead of solving the problem exactly at each time step, we only apply a finite number of iterations at each time step. Commonly used notions of regret are used to measure the performance of the online algorithm. Moreover, coordinate descent algorithms with different updating rules are considered, including both deterministic and stochastic rules that are developed in the literature of classical offline optimization. A thorough regret analysis is given for each case. Finally, numerical simulations are provided to illustrate the theoretical results. [ABSTRACT FROM AUTHOR]

Large-scale discrete ordinates neutron transport calculations are known to be excessively slow due to the large number of grids that need to be solved and the limitations of the source iteration method employed. This paper introduces a novel algorithm called DMD-MLTG as a practical means for solving this problem. The DMD-MLTG algorithm utilizes the dynamic mode decomposition (DMD) method to generate low-order approximation operators from historical iteration calculations, introducing only a minimal amount of additional processing in the multilevel octree grid (MLTG) algorithm. Numerical results from a self-designed one-group problem and crooked pipe problem demonstrate that the DMD-MLTG algorithm shows superior performance and robustness in challenging situations. The HBR-2 benchmark results confirm that the DMD-MLTG algorithm outperforms the MLTG algorithm by reducing computation time by 50%. The DMD-MLTG algorithm offers a promising solution to the issue of slow large-scale discrete ordinates neutron transport calculations, making it a valuable tool for practical shielding problems. • Combining dynamic mode decomposition method with multilevel octree grid algorithm. • Suitable for SI systems with high scattering ratio media that are difficult to converge. • Compatible with heterogeneous media and general spatial discretization schemes. • Making large-scale discrete ordinates neutron transport calculations more efficient. [ABSTRACT FROM AUTHOR]

A new algorithm is proposed in this paper for computing the nearest polynomial to multiple given polynomials with a given zero in the real case, where the distance between polynomials is defined by the weighted ℓ 2 , q norm (0 < q ≤ 2). First, the problem is formulated as a univariate constrained non-convex minimization problem, where prior information of the coefficients of the polynomial can be embedded by selecting proper weights. Then, an iteratively reweighted algorithm is designed to solve the obtained problem, and also the convergence and rate of convergence are uniformly demonstrated for all q in (0 , 2 ]. Since all the existing methods for computing the nearest polynomial to multiple given polynomials with a given zero are limited to the real case, we ingeniously extend the results to the complex case. Finally, two representative examples that separately compute the nearest real and complex polynomials are presented to show the effectiveness of the proposed algorithm. • A weighted ℓ 2 , q -norm based minimization problem is constructed with prior information of polynomial embedded. • After converting the problem into a univariate problem, an iteratively reweighted algorithm is proposed to solve it. • Using properties of a concave function, convergence and rate of convergence of the proposed algorithm are provided. • It is the first time to study computation of the nearest polynomial to multiple given polynomials with a given zero in the complex case. [ABSTRACT FROM AUTHOR]

• A new multi-objective multiple robots task assignment problem is studied. • A multi-objective mixed integer programming model is established. • An effective multi-objective evolutionary algorithm to solve this problem. This paper addresses a multiple agricultural spraying robots task assignment problem in the greenhouse environment. The objective of the problem is to obtain a set of Pareto solutions that simultaneously optimize the total travel distance and maximum completion time of all robots. To solve this problem, an effective multi-objective evolutionary algorithm is proposed. In the proposed algorithm, an initial population with high quality and diversity is generated by a heuristic allocation strategy based on robot capacity constraints. During the evolutionary phase, a crossover strategy based on information in the non-dominated solution set is designed for exploration in the global scope. A multi-objective local search with an iterated greedy idea is introduced to improve the exploration ability of the algorithm. Meanwhile, a restart operator based on the ideal point is presented to jump out of the local optimum. Finally, extensive experiments based on different scales are conducted. The results show that the proposed algorithm significantly outperforms several state-of-the-art multi-objective algorithms in the literature. [ABSTRACT FROM AUTHOR]

Multi-objective programming problem often contains numerous efficient solutions, which confuses the decision-maker. To assist in selecting the most desirable solution, optimizing a function over the efficient set becomes crucial. In this paper, we present a novel method for optimizing a general quadratic function over the efficient set of a multi-objective integer linear programming problem. To solve this problem, a ranking approach and efficiency test is utilized. The proposed methodology obtains a globally optimal solution by systematically scanning ranked solutions of an integer quadratic programming problem until the efficiency condition is satisfied. For generating ranked solutions, we construct a related integer linear programming problem. Then, ranked solutions of the integer linear programming problem are used for enumerating ranked solutions of the integer quadratic programming problem. The convergence of our algorithm is established theoretically, and its steps are illustrated using a numerical example. Aparticular case of the proposed method for optimizing a linear function over the efficient set of a multi-objective integer linear programming problem is also discussed. Further, extensive computational results demonstrate the effectiveness of our method for solving problems with large number of constraints, variables, and objective functions. Moreover, comparative analysis shows that the developed algorithm came out to be computationally more efficient as compared to the existing state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Traditional clustering algorithms such as K-means are highly sensitive to the initial centroid selection and perform poorly on non-convex dataset. To address these problems, a novel data adaptive Bregman clustering algorithm (DBGSA) is proposed in this paper, which combines the universal gravitational algorithm to bring similar points closer in the dataset. In addition, we introduce the Bregman divergence generalized power mean information loss minimization to identify cluster centers and build a hyperparameter identification optimization model, which effectively solves the problems of manual adjustment and uncertainty in the improved dataset. Extensive experiments are conducted on four simulated datasets and six real datasets. The results demonstrate that DBGSA significantly improves the accuracy of various clustering algorithms by an average of 63.8% compared to the contrasted algorithms, making it applicable to diverse datasets. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce new iterative algorithms for approximating a solution to a class of monotone operator equations. More precisely, we study the split common solution problem with multiple output sets for monotone operator equations in Hilbert spaces. In order to solve this problem, we propose three new algorithms and establish strong convergence theorems for them. [ABSTRACT FROM AUTHOR]

Edge computing is a key technology in computational imaging, where the algorithms determine reconstruction quality and reconstruction speed. Recovering spectral information from ill-conditioned data of coded aperture snapshots is a novel but challenging solution in spectral imaging for dynamic scenes. Further study is necessary to enhance the edge computing power of computational spectral programs. Deep unfolding networks have made considerable headway in this direction, but the existing methods still have two drawbacks: (1) The 3D spectral cube exhibits long-range dependency and non-local self-similarity in both spatial and spectral dimensions, yet learn the global features of spatial and spectral is difficult, which remains to be investigated. (2) The global features degenerate as the number of iterations increases. To solve the above problems, this paper proposes a Momentum Accelerated Unfolding Network with Spectral–Spatial prior (MAUNSS). Specifically, a denoising module based on a spectral–spatial transformer is designed, which uses both sparse spectral features to recover global information and dense spatial features to enhance detail texture. Within such a framework, cross-stage feature transmission channels transmitting features across spectral and spatial transformers at different stages are built to avoid feature degeneration. In order to increase the training speed and save the time and cost associated with the iterative approach, we improve it by means of the momentum acceleration module. Supplemented by a re-projection loss, a technique introduced from 3D measurement, the accuracy is further improved. To the best of our knowledge, the proposed method is an attempt to use momentum to expedite the convergence of unfolding networks. Extensive experiments demonstrate that our proposed method outperforms previous state-of-the-art methods by more than 1.94 dB. • MAUN with learnable hyper-parameters and Momentum methods ensures better accuracy. • A reprojection loss is proposed to improve the reconstruction accuracy of HIS. • MAUNSS exceeds the previous SOTA methods while requiring less memory costs. [ABSTRACT FROM AUTHOR]

Box-constrained optimization problems in the real m × n matrix space have been widely applied in big data mining. However, efficient solution of them is still a challenge. In this paper, a new nonmonotone line search rule is first proposed by extending the well-known ones and inheriting their advantages. Then, by analyzing and exploiting properties of this rule, a new nonmonotone spectral projected gradient algorithm is developed to solve the box-constrained optimization problems in the matrix space. Global convergence of the developed algorithm is also established. Numerical tests are conducted on a series of randomly generated test problems and those in the set of benchmark test problems. Compared with other existing nonmonotone line search rules, our rule shows its advantages in terms of the significantly reduced number of function evaluations and significantly reduced number of iterations. To further validate applicability of this research, we apply the studied optimization problem and the developed algorithm to solve the problems of image clustering. Numerical results demonstrate that the proposed method can generate better clustering results and is more robust than the similar ones available in the literature. [ABSTRACT FROM AUTHOR]

The Minimum Vertex Cover P roblem is the optimization problem of finding a vertex cover V c of minimal cardinality in a given graph. It is a classic NP-hard problem, and various algorithms have been suggested for it. In this paper, we start with a basic algorithm for solving the problem. Using a probabilistic idea, we use it to develop an improved algorithm. The algorithm is greedy; at each step it adds to the cover a vertex such that the expected cover size, if we continue randomly after this step, is minimal. We study the new algorithm theoretically and empirically, and run simulations to compare its performance to that of some algorithms of a similar nature. [ABSTRACT FROM AUTHOR]

In this paper, we propose an improved version of Twin SVM using a non-smooth optimization method. Twin SVM generally consists in determining two non-parallel planes by alternately solving two constrained optimization models. Solving this problem using the classical Lagrangian method has many limitations, notably: its only limited to handle Gaussian noise, generally exaggerates the influence of outliers and cannot handle unbalanced data, this due to the differentiability of the model. To circumvent these issues, we transform two-constraint optimization models using the penalty method into an unconstrained non-smooth optimization one. The non-smoothness nature of the problem has many advantages, but it requires special treatment, which is why we use the primal dual method to solve it, since it is the most appropriate and it is robust in terms of stability, convergence and speed (Lyaqini, Nachaoui and Hadri, 2022). To demonstrate the effectiveness of the proposed approach, several experiments were carried out on numerous UCI benchmarks, medical image and HandPD datasets. These experiments demonstrated the effectiveness and applicability of the proposed approach, with satisfactory results compared to the state of the art. [ABSTRACT FROM AUTHOR]

Decomposition-based multiobjective evolutionary algorithms are widely utilized for solving multiobjective optimization problems (MOPs). These algorithms have a higher degree of diversity due to a uniform distribution of the reference vectors in the objective space. However, these algorithms may struggle to select a suitable candidate set for solving many-objective optimization problems (MaOPs) with irregular Pareto fronts (PFs). To solve this problem, a solution potential-based adaptation reference vector evolutionary algorithm (SPARVEA) is proposed in this paper. In the algorithm, a concept called solution potential is presented to assess whether the direction of convergence of the solution to the ideal solution has potential. The solution potential is obtained with an evaluation function to calculate the potentials of the corresponding solutions. A solution potential-based adaptation strategy is then designed for adapting the reference vector to better guide the direction of convergence of the solution when solving MaOPs with irregular PFs. A modified angle penalty distance (mAPD) is adjusted to improve the convergence rate of the solution set in many-objective optimization problems. The proposed algorithm SPARVEA is compared with state-of-the-art many-objective evolutionary algorithms on four test sets with irregular PFs and four practical problems. Experimental results demonstrate the superior performance of SPARVEA on many-objective optimization problems. This contribution helps to advance the development of efficient algorithms for solving many-objective optimization problems. • SPARVEA is proposed for solving many-objective optimization problems with irregular Pareto fronts. • A concept of solution potential is evaluated to assess the direction of the solution to the ideal point. • A solution potential-based adaptation strategy is presented to adjust the reference vectors to guide the direction of convergence of the solution. • A modified angle penalty distance is adapted to control the convergence rate of the population. • The SPARVEA outperforms other contrasting state-of-the-art many-objective optimization algorithms. [ABSTRACT FROM AUTHOR]

For real-world design optimization, there may often exist multiple candidate concepts that may solve the problem at hand. The process of concurrently identifying the best concept and the corresponding variable values that optimize the design objective(s) is termed as a multi-concept optimization (MCO). While such problems are commonly encountered in practical domains such as engineering, transport, product design, there has been little focus on developing computationally efficient algorithms for MCO. One of the reasons contributing to this gap is the scarcity of benchmark problems with diverse challenges that could be used as a testbed for the development and systematic evaluation of advanced MCO algorithms. In this paper, we particularly focus on MCO problems with multiple conflicting objectives. We review the existing multi-objective MCO test problems and identify their shortcomings through preliminary numerical experiments. Then, we propose a methodology and provide a test problem generator for systematically constructing new multi-objective MCO instances with desired properties. We also propose 28 specific test problem instances using this generator to build a benchmark suite that poses a wide range of challenges to the prospective search strategies. Further, we conduct numerical experiments on the proposed test suite using multiple existing algorithmic strategies from the literature to demonstrate their performance. The results clearly highlight the challenges faced by the strategies for different types of problems. These observations emphasize the need for research efforts towards the development of more efficient and versatile MCO algorithms to tackle a wider range of MCO problems. [ABSTRACT FROM AUTHOR]

Many engineering optimization problems fall into the category of Mixed-Integer Nonlinear Programming (MINLP) problems, which combine nonlinear relations, constraint conditions, and different types of variables, including continuous, integer, and/or discrete variables. Solving MINLP problems can be a challenging exploration process since their landscape can be composed of many discontinuous feasible parts with different sizes. In this scheme, Evolutionary Algorithms (EAs) often fail to generate enough diversity to explore the discontinuous feasible parts. Consequently, EAs are vulnerable to being attracted to larger discontinuous feasible parts, even if they are not promising regions. In this paper, a new proposal based on two fundamental strategies is presented to improve the performance of the differential evolution algorithm when solving MINLP problems. The first strategy considers a set of "good fitness-infeasible solutions" that contribute to exploring promising regions from infeasible contours. It reduces the vulnerability of the solutions to be attracted to larger discontinuous feasible parts with unpromising objective function values. The second is a composite trial vector generation to improve the combinatorial exploration while ensuring a robust convergence capacity toward the final solution. Sixteen well-known MINLP problems are used in several experiments to evaluate the performance of the proposed algorithm, comparing it to state-of-the-art EAs. The results provided by the proposal show a better performance in terms of quality, robustness, and computational cost. • Solving mixed-integer nonlinear programming problems can be challenging. • Improved differential evolution for mixed-integer nonlinear programming problems. • Good fitness-infeasible solutions contribute to exploring the smaller feasible regions. [ABSTRACT FROM AUTHOR]