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This paper presents an improved swarming algorithm that enhances low-illumination images. The algorithm combines a hybrid Harris Eagle algorithm with double gamma (IHHO-BIGA) and incomplete beta (IHHO-NBeta) functions. This paper integrates the concept of symmetry into the improvement steps of the image adaptive enhancement algorithm. The enhanced algorithm integrates chaotic mapping for population initialization, a nonlinear formula for prey energy calculation, spiral motion from the black widow algorithm for global search enhancement, a nonlinear inertia weight factor inspired by particle swarm optimization, and a modified Levy flight strategy to prevent premature convergence to local optima. This paper compares the algorithm's performance with other swarm intelligence algorithms using commonly used test functions. The algorithm's performance is compared against several emerging swarm intelligence algorithms using commonly used test functions, with results demonstrating its superior performance. The improved Harris Eagle algorithm is then applied for image adaptive enhancement, and its effectiveness is evaluated on five low-illumination images from the LOL dataset. The proposed method is compared to three common image enhancement techniques and the IHHO-BIGA and IHHO-NBeta methods. The experimental results reveal that the proposed approach achieves optimal visual perception and enhanced image evaluation metrics, outperforming the existing techniques. Notably, the standard deviation data of the first image show that the IHHO-NBeta method enhances the image by 8.26%, 120.91%, 126.85%, and 164.02% compared with IHHO-BIGA, the single-scale Retinex enhancement method, the homomorphic filtering method, and the limited contrast adaptive histogram equalization method, respectively. The processing time of the improved method is also better than the previous heuristic algorithm. [ABSTRACT FROM AUTHOR]

This article delves into the critical realm of modern computer cluster management. It focuses on the effect that the increasing heterogeneity of the clusters has on the workload managers. The proposed schedulers consider node properties instead of job properties to make decisions, which is something not currently done by mainstream scheduling algorithms. In order to increase the knowledge in this topic, this paper proposes two novel algorithms whose main task is to choose the best compute nodes to schedule the incoming jobs. To this effect, they exclusively take into account the properties of the nodes, instead of the common trend of considering the properties of the jobs. The experimental results show that these algorithms outperform well-known heuristic algorithms found in the literature. [ABSTRACT FROM AUTHOR]

The besiege and conquer algorithm has shown excellent performance in single-objective optimization problems. However, there is no literature on the research of the BCA algorithm on multi-objective optimization problems. Therefore, this paper proposes a new multi-objective besiege and conquer algorithm to solve multi-objective optimization problems. The grid mechanism, archiving mechanism, and leader selection mechanism are integrated into the BCA to estimate the Pareto optimal solution and approach the Pareto optimal frontier. The proposed algorithm is tested with MOPSO, MOEA/D, and NSGAIII on the benchmark function IMOP and ZDT. The experiment results show that the proposed algorithm can obtain competitive results in terms of the accuracy of the Pareto optimal solution. [ABSTRACT FROM AUTHOR]

This paper presents an improved arithmetic optimization algorithm that incorporates hybrid elite pool strategies to address the limitations of the arithmetic optimization algorithm (AOA). In AOA, the linear mathematical optimization acceleration (MOA) function cannot balance global exploitation and local exploration well. Therefore, the accuracy and convergence speed of the algorithm cannot be guaranteed. To improve the performance of AOA, this paper reconstructed a nonlinear MOA function, which is expected to balance the exploitation and the exploration of AOA. Furthermore, four hybrid elite pool strategies are integrated to enhance the ability to escape local optima. The proposed algorithm inherits the fast convergence of AOA and develops the performance of escaping local optima. Numerical experiment results on benchmark functions and engineering problems show that the proposed algorithm outperforms other compared meta-heuristic algorithms in terms of convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

Improving production processes includes not only activities concerning manufacturing itself, but also all the activities that are necessary to achieve the main objectives. One such activity is transport, which, although a source of waste in terms of adding value to the product, is essential to the realization of the production process. Over the years, many methods have been developed to help manage supply and transport in such a way as to reduce it to the necessary minimum. In the paper, the problem of delivering components to a production area using trains and appropriately laid-out carriages was described. It is a milk run stop locations problem (MRSLP), whose proposed solution is based on the use of heuristic algorithms. Intelligent solutions are getting more and more popular in the industry because of the possible advantages they offer, especially those that include the possibility of finding an optimum local solution in a relatively short time and the prevention of human errors. In this paper, the applicability of three algorithms -- tabu search, genetic algorithm, and simulated annealing -- was explored. [ABSTRACT FROM AUTHOR]

Community structure is a very interesting attribute and feature in complex networks, which has attracted scholars' attention and research on community detection. Many single-objective optimization algorithms have been migrated and modified to serve community detection problems. Due to the limitation of resolution, the final algorithm implementation effect is not ideal. In this paper, a multi-objective community detection method based on a pigeon-inspired optimization algorithm, MOPIO-Net, is proposed. Firstly, the PIO algorithm is discretized in terms of the solution space representation, position, and velocity-updating strategies to adapt to discrete community detection scenarios. Secondly, by minimizing the two objective functions of community score and community fitness at the same time, the community structure with a tight interior and sparse exterior is obtained. Finally, for the misclassification caused by boundary nodes, a mutation strategy is added to improve the accuracy of the final community recognition. Experiments on synthetic and real networks verify that the proposed algorithm is more accurate in community recognition compared to 11 benchmark algorithms, confirming the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The utilization of cloud computing environments is highly popular for carrying out workflow executions due to its ability to provide clients with immediate access to computing resources. Among the various workflow scheduling problems in the cloud, deadline-constrained workflow scheduling has garnered increasing attention in recent years. This paper introduces a hybrid scheduling algorithm known as Partial Critical Path–Ant Colony Optimization (PCP–ACO), which aims to minimize the execution cost of a workflow while ensuring that it meets the user-defined deadline in cloud environments. PCP–ACO is a list scheduling algorithm that combines the PCP heuristic algorithm with the meta-heuristic ACO to achieve faster convergence. The list scheduling algorithm consists of two phases: task ordering and resource selection. In the case of PCP–ACO, the first step involves calculating a topological sort of the workflow tasks to assign priority to each task. Subsequently, the ACO meta-heuristic is employed to allocate the appropriate resource to each task of the workflow, based on their respective sub-deadlines that are computed using the PCP heuristic. In order to evaluate the effectiveness of the proposed algorithm, several experiments were conducted using five real scientific workflows. The results demonstrate that PCP–ACO outperforms the IC-PCP, L-ACO, and HP-GA algorithms in terms of average execution cost, achieving reductions of 19%, 17.3%, and 21.5%, respectively. [ABSTRACT FROM AUTHOR]

Heat exchanger network (HEN) synthesis is a vibrant research field in process system engineering, with substantial contributions to energy conservation and emissions reduction initiatives. The optimal design of a heat exchanger network is not an easy task due to the abundance of local optima in the solution space caused by the non-linear, non-convex, and discontinuous nature of the problem. Generally, several heuristic algorithms employ a greedy evolutionary mechanism, optimize through greedily accepting the decrease in the objective function, and converge to obtain the optimal solution. The Random Walk algorithm has a simple evolutionary mechanism, is prone to mutation, and exhibits high flexibility. However, the algorithm's inherent persistent greediness in searching restrict the scope of the search. Thus, this paper proposes an anti-greedy concept based on the Random Walk method to serve as the basis of a new synthesis approach called the Anti-greedy Random Walk algorithm. Two strategies are proposed in the algorithm, which broaden the solution domain by slowing down rapid unit reduction and accepting imperfect solutions, respectively. One strategy is to thoroughly search for the integer and continuous variables of the HEN problem by covering a much larger search space. Another is to escape the local extrema and move forward to discover more possibilities. Quantitative data demonstrates the algorithm's ability to avoid the local extrema and enhance the search effectiveness. Three different scales of classical cases are used in this work and the obtained results are superior to the published ones. [Display omitted] • An improved Random Walk algorithm is presented for heat exchanger network synthesis. • Two anti-greedy strategies are proposed from an objective and structural perspective. • Both integer and continuous variables are explored by covering a larger search space. • Three benchmark cases were solved with better results than previously literature. [ABSTRACT FROM AUTHOR]

The paper proposes a topology-free specification of distributed control systems by means of a process-oriented programming paradigm. The proposed approach was characterized, on the one hand, by a topologically independent specification of the control algorithm and, on the other hand, by the possibility of using existing formal verification methods by preserving the semantics of a centralized process-oriented program. The paper discusses the advantages of a topologically independent specification of distributed control systems, outlines the features of control software, argues why the use of a process-oriented approach to the development of the automation of cyber-physical systems is suitable for solving these problems, describes a general scheme for implementing a distributed control system according to a process-oriented specification, and proposes a formal heuristic algorithm for partitioning a sequential process-oriented program into independent clusters. We illustrate our algorithm with bottle-filling and sluice case studies. [ABSTRACT FROM AUTHOR]

Structure learning is the core of graph model Bayesian Network learning, and the current mainstream single search algorithm has problems such as poor learning effect, fuzzy initial network, and easy falling into local optimum. In this paper, we propose a heuristic learning algorithm HC-PSO combining the HC (Hill Climbing) algorithm and PSO (Particle Swarm Optimization) algorithm, which firstly uses HC algorithm to search for locally optimal network structures, takes these networks as the initial networks, then introduces mutation operator and crossover operator, and uses PSO algorithm for global search. Meanwhile, we use the DE (Differential Evolution) strategy to select the mutation operator and crossover operator. Finally, experiments are conducted in four different datasets to calculate BIC (Bayesian Information Criterion) and HD (Hamming Distance), and comparative analysis is made with other algorithms, the structure shows that the HC-PSO algorithm is superior in feasibility and accuracy. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs), integral components underpinning the infrastructure of the internet of things (IoT), confront escalating threats originating from attempts at malicious jamming. Nevertheless, the limited nature of the hardware resources in distributed, low-cost WSNs, such as those for computing power and storage, poses a challenge when implementing complex and intelligent anti-jamming algorithms like deep reinforcement learning (DRL). Hence, in this paper a rapid anti-jamming method is proposed based on imitation learning in order to address this issue. First, on-network nodes obtain expert anti-jamming trajectories using heuristic algorithms, taking historical experiences into account. Second, an RNN neural network that can be used for anti-jamming decision making is trained by mimicking these expert trajectories. Finally, the late-access network nodes receive anti-jamming network parameters from the existing nodes, allowing them to obtain a policy network directly applicable to anti-jamming decision making and thus avoiding redundant learning. Experimental results demonstrate that, compared with traditional Q-learning and random frequency-hopping (RFH) algorithms, the imitation learning-based algorithm empowers late-access network nodes to swiftly acquire anti-jamming strategies that perform on par with expert strategies. [ABSTRACT FROM AUTHOR]

In this paper, in order to reduce the energy consumption and time of data transmission, the non-orthogonal multiple access (NOMA) and mobile edge caching technologies are jointly considered in mobile edge computing (MEC) networks. As for the cache-assisted vehicular NOMA–MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under time and resource constraints, which jointly optimize the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task-caching algorithm based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm could achieve lower local energy consumption than several benchmark algorithms, and HA could achieve lower consumption than the completely offloading algorithm and local execution algorithm. [ABSTRACT FROM AUTHOR]

There is a widespread use of cluster-based routing in wireless sensor networks since it is the most energy-efficient. Idealizing cluster heads, on the other hand, is NP-hard and hence requires heuristic or metaheuristic approaches. However, while outperforming algorithms, metaheuristics computation time restricts its ability to respond to routing requests as rapidly as algorithms can today. A network's or an application's parameters can't be easily accommodated by routing methods. This paper offers the HMML, a combination model combining hybrid metaheuristics and machine-learning. Our HMML model makes use of an automated tuning metaheuristic (e.g. evolutionary algorithm) to fine-tune the heuristic technique for each specific configuration. For a variety of combinations, this is done. A network simulation is run using the modified heuristic algorithm in each configuration to arrive at a solution. As a result, a comprehensive dataset for a variety of conditions is produced (e.g., support vector machine). These characteristics include local (round-state), global (network-state), and application-specific aspects of the input feature vector. After training, the HMML model may be used to quickly cluster data. Machine learning's capacity to generalize helps us comprehend the metaheuristic algorithm's behavior in identifying optimal paths for previous configurations. Simulation studies show that HMML can adapt to varied applications while extending network life which increases upto 5% for total energy consumption. [ABSTRACT FROM AUTHOR]

This paper deals with fuzzy multi-depot bus scheduling (FMDBS) problem in which the objective function and constraints are defined with fuzzy attributes. Credibility relation is used to formulate the problem as an integer multicommodity flow problem. A novel combination of branch-and-price and heuristic algorithms, is proposed to efficiently solve FMDBS problem. In the proposed algorithm, the heuristic algorithm is applied to generate initial columns for the column generation method. Also, a heuristic algorithm is used to improve the generated solutions in each node of the branch-and-price tree. Two sets of benchmark examples are applied to demonstrate the efficiency of the proposed algorithm for large-scale instances. Also, the algorithm is applied to solve the classical multi-depot bus scheduling problem. The results show that the proposed algorithm decreases integrality gap and computational time in comparison with the state-of-the-art algorithms and normal branch-and-price algorithm. Finally, as a case study, the bus schedules in Tehran BRT network are generated. [ABSTRACT FROM AUTHOR]

This paper discusses and compares several computed tomography (CT) algorithms capable of dealing with incomplete data. This type of problem has been proposed for a symmetrical grid and symmetrically distributed transmitters and receivers. The use of symmetry significantly speeds up the process of constructing a system of equations that is the foundation of all CT algebraic algorithms. Classic algebraic approaches are effective in incomplete data scenarios, but suffer from low convergence speed. For this reason, we propose the use of nature-inspired algorithms which are proven to be effective in many practical optimization problems from various domains. The efficacy of nature-inspired algorithms strongly depends on the number of parameters they maintain and reproduce, and this number is usually substantial in the case of CT applications. However, taking into account the specificity of the reconstructed object allows to reduce the number of parameters and effectively use heuristic algorithms in the field of CT. This paper compares the efficacy and suitability of three nature-inspired heuristic algorithms: Artificial BeeColony (ABC), Ant Colony Optimization (ACO), and Clonal Selection Algorithm (CSA) in the CT context, showing their advantages and weaknesses. The best algorithm is identified and some ideas of how the remaining methods could be improved so as to better solve CT tasks are presented. [ABSTRACT FROM AUTHOR]

With the continuous improvement of the network hardware environment, people turn the demand target to the network application environment and the construction of information resources. How to build a network teaching platform for general undergraduate teaching to ensure the stability of the system and high-quality services during operation especially large-scale concurrent access will inevitably lead to the increase in the business volume of each core part of the network, the number of visits, and data traffic. With the growth of the network, the corresponding processing power and computing intensity also increase rapidly, which causes problems such as overloading of core network equipment, network bottlenecks, and network congestion. Simply pursuing high-performance hardware to solve problems will undoubtedly result in high cost investment; moreover, equipment with excellent performance cannot meet the needs of the current rapidly growing business volume. According to the design goal of the dance online teaching platform, to meet the online teaching load requirements of many people at the same time, the pressure of the web server cluster must be great. Because many people in online at the same time put too much pressure on the web server, this part of the network cannot be processed in time, which leads to the phenomenon that the performance of this part and even the whole network is degraded. In severe cases, it will even cause network communication services to come to a standstill, that is, the so-called deadlock phenomenon. If the protocol software cannot detect congestion and reduce the packet sending rate, the network will be paralyzed due to congestion. This situation will cause the problem of movement delay for online dance teaching, which will seriously affect the quality of teaching. Therefore, the dance online course system should be continuously improved, the quality of online courses should be continuously improved, and the study of the practical application of load balancing technology in the network teaching environment has become an important means to solve the relationship between supply and demand of network teaching. According to the experimental analysis, when the number of Worker' actuators is fixed, the execution time span of MakeSpan increases with the increase of tasks, while the time required by the optimized load balancing algorithm proposed in this paper increases by 1.32 s on average with the increase of tasks, and the time required by heuristic algorithm and bee colony algorithm increases by 3.68 s and 3.45 s on average with the increase of tasks. On the whole, the optimized load balancing algorithm proposed in this paper has obvious advantages. [ABSTRACT FROM AUTHOR]

For nearly a decade, quality-related fault detection algorithms have been widely used in industrial systems. However, the majority of these detection strategies rely on static assumptions of the operating environment. In this paper, taking the time series of variables into consideration, a dynamic kernel entropy component regression (DKECR) framework is proposed to address the instability of quality-related fault detection due to the existing dynamic characteristics. Compared with the typical kernel entropy component analysis method, the proposed method constructs the relationship between process states and quality states to further interpret the direct effect on the product taken by the fault. In the proposed approach, process measurements are converted to a lower-dimensional subspace with a specific angular structure that is more comprehensive than traditional subspace approaches. In addition, the angular statistics and their relevant thresholds are exploited to enhance the quality-related fault detection performance. Finally, the proposed method will be compared with three methods by means of a numerical example and two industrial scenarios to demonstrate its practicality and effectiveness. Note to Practitioners—This paper studies a quality-related fault detection problem for the dynamic nonlinear industrial system. Controlling and measuring the quality state is challenging for the high-level monitoring system of the manufacturing process due to the nonlinear dynamic feature in states. This paper proposes a new data-driven method based on the kernel entropy component analysis method to assess the correlation between the quality and fault in the industrial system, reducing unnecessary overhaul and maintenance. Based on the autoregressive moving average exogenous algorithm, the proposed method captures the dynamic interaction between the process states to decrease false alarms. In the experimental section, the DKECR method outperforms the compared approaches, which can provide stable fault detection results. Additionally, the unique angle structure of the proposed method can supply more information for engineers’ monitoring needs. [ABSTRACT FROM AUTHOR]

This paper addresses a UAV path planning problem for a team of cooperating heterogeneous vehicles composed of one unmanned aerial vehicle (UAV) and multiple unmanned ground vehicles (UGVs). The UGVs are used as mobile actuators and scattered in a large area. To achieve multi-UGV communication and collaboration, the UAV, modeled as a Dubins vehicle, serves as a messenger to fly over the effective communication range of all UGVs to relay information. The curvature-constrained path planning of the messenger UAV is formulated as a Dubins Traveling Salesman Problem with Dynamic Neighborhood (DTSPDN) which is a complex optimization problem involving coupled variables and contains dynamic constraints. We design an effective memetic algorithm to find the shortest route that enables the messenger UAV to visit all moving UGVs. This algorithm combines the genetic algorithm procedure, two kinds of local search operators based on gradient search and uniform sampling respectively, and a gradient-based repair operator to repair the solutions violating dynamic constraints. During the evolutionary process, a special phenomenon may occur that changing some decision variables (i.e., visiting sequence and location) may not affect the evaluation function value, but may alter the feasible region of another decision variable (i.e., visiting time) due to the encounter constraint between the UAV and UGV. To track and utilize the change of the feasible region, a transformation procedure is proposed to change one solution to another with less visiting time by analyzing the encounter pattern between UAV and UGV. The computational results on random instances with different scales demonstrate that the proposed approach can effectively generate better curvature-constrained tours to encounter all moving UGVs when compared to other four competitive algorithms in the literature. Note to Practitioners—This paper studies an emerging path planning problem for a UAV which is used to provide communication service for multiple moving UGVs. These UGVs are required to execute tasks (e.g., firefighting, search and rescue) within a large area. Due to their limited communication capabilities, they may be unable to obtain necessary information from other UGVs. The UAV serves as a messenger to fly over the effective communication range of all moving UGVs to relay information. We propose a novel memetic algorithm to efficiently search for the shortest tour that enables the messenger UAV to visit all moving UGVs. The memetic algorithm combines the parallel global search virtue of genetic algorithm with efficient local search procedure to improve the generated tour. A gradient-based repair procedure is also employed to make sure that the planned tour can guide the UAV to sequentially encounter each moving UGV. Simulations exhibit that the proposed approach can effectively generate high-quality tours for messenger UAV to rapidly visit all UGVs, which assists UGVs to achieve collaboration in large area. In future work, the proposed memetic algorithm will be extended to plan tours for multiple messenger UAVs. [ABSTRACT FROM AUTHOR]

This paper introduces a modified slime mould algorithm to enhance the exploitation and exploration of the algorithm. As stated in no free lunch theorem no single algorithm can proficiently solve the optimization problems at hand. Hence, hybridization of more than one heuristic algorithm is employed to tackle the situation. The proposed strategy employs opposition-based learning and wavelet mutation-based strategy to the basic slime mould algorithm for exploring the search space while avoiding local optima. These methods are tested on standard benchmark functions, CEC-14 functions and different single and multiobjective thermal load dispatch problems including small, medium and large-scale problems while satisfying non-differentiable constraints namely valve point loading, prohibited operating zone and ramp rate limit which make objective function non-convex and discontinuous. The pareto dominance technique is employed to achieve the optimal solution in multiobjective problems. This paper also compares the performance of the proposed technique with other methods available in the literature for different economic load dispatch problems and emission dispatch problems. The Wilcoxon signed-rank test performed to confirm the competitiveness of the proposed method for independent samples. [ABSTRACT FROM AUTHOR]

The paper aims at mitigating hot-spots during Offline Scheduling in IaaS (Infrastructure-as-a-Service) cloud systems. Unlike previous studies, the research focuses on identifying and resolving hot-spots not at servers, but at server racks. A two-phase algorithm for performing power-aware offline scheduling is proposed. The first phase aims at identifying and mitigating hot-spots at racks, while the second phase performs VM consolidation, i.e. minimization of the number of occupied servers while maintaining a feasible VM mapping and low migration costs. The proposed algorithm takes into account the dynamic nature of VM's resource consumption: it does not only resolve detected hot-spots, but also tries to avoid hot-spots in a reasonable future time period. The algorithm was tested with the data from a real IaaS cloud with different sets of algorithm's parameters. Experimental evaluation showed that the statistical estimates of the future VM's resource consumption provide the most reliable mapping, which is a result of minimization of the number of new hot-spot occurrences. • A server rack is a hot-spot if it exceeds the upper bound of power consumption. • Hot-spot identification, mitigation, and avoidance is crucial for IaaS systems. • An algorithm is proposed for rack hot-spot mitigation and consolidation of VMs. • Considering future CPU utilization values is associated with migrating more memory. • Ignoring future CPU utilization values leads to more new hot-spots happening shortly. [ABSTRACT FROM AUTHOR]

The performance growth in processors has been continuing toward increasing the number of processing cores on the chip and scaling the feature size of transistors. However, in the nanoera, side effects of the scaling, such as induced heterogeneities in the performance, power, and soft error rate of identically designed cores, prevent the potential performance from being fully utilized. In this paper, we harness the mentioned side effects in shared-memory multicore processors with unknown workloads by a dynamic heuristic scheduling algorithm called HDSAP. HDSAP aims to maximize performance, i.e., the average response time, under power and reliability constraints in presence of induced heterogeneities. In this regard, we use a mathematical model to quantify task to core assignments based on performance variation. We also consider the variation in power to change selected cores when the power constraint is missed. To meet the reliability constraint, we use N-modular redundancy while being aware of the variation in the soft error rate of cores to prevent under/over reliability estimation. To evaluate HDSAP, we run SPLASH benchmark suite on Sniper and MACPat simulators. As a result, the response time of HDSAP reduces by 6%, 8%, and 25% in comparison with similar algorithms under the same power and reliability constraints. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for network slicing in 5G backhaul networks, targeting services with low or very low latency requirements. We propose a modified A* algorithm that incorporates network quality of service parameters into a composite metric. The algorithm's efficiency outperforms that of Dijkstra's algorithm using a precalculated heuristic function and a real-time monitoring strategy for congestion management. We integrate the algorithm into an SDN module called a path computation element, which computes the optimal path for the network slices. Experimental results show that the proposed algorithm significantly reduces processing time compared to Dijkstra's algorithm, particularly in complex topologies, with an order of magnitude improvement. The algorithm successfully adjusts paths in real-time to meet low latency requirements, preventing packet delay from exceeding the established threshold. The end-to-end measurements using the Speedtest client validate the algorithm's performance in differentiating traffic with and without delay requirements. These results demonstrate the efficacy of our approach in achieving ultra-reliable low-latency communication (URLLC) in 5G backhaul networks. [ABSTRACT FROM AUTHOR]

The two-dimension strip packing problem (2DSPP) is an NP-hard combinatorial optimization problem, and it has a wide range of applications. The order in which the rectangles are placed in the strip is the key to solving the 2DSPP. In this paper, the corner increment multi-level scoring strategy and the two-step selection strategy are proposed to make the selection of rectangles more reasonable. The hierarchical construction strategy is used to improve the constructive heuristic algorithm to expand the search space of the rectangle selection. Based on this, this paper proposes a multi-strategy rectangle selection algorithm TSMSA for solving 2DSPP. Comparing experiments with 7 excellent algorithms for solving 2DSPP on 737 instances of the benchmark dataset shows that the instance number of the optimal solution obtained by TSMSA is 1.05~2.9 times that of the other 7 algorithms. [ABSTRACT FROM AUTHOR]

Decentralized storage of data is one of the typical applications in the blockchain network. However, most of the existing works neglected the storage balancing problem in the blockchain network, which has an immediate impact on the availability and stability of the network. Therefore, this paper proposes a storage balancing problem for non-local data storage in the blockchain network and proves that the problem is non-deterministic polynomial (NP)-hard. The criterion of the storage balance is established by a balanced coefficient in the proposed scheme. A heuristic matching algorithm (HMA), a genetic algorithm (GA) and a tabu search algorithm (TSA) are customized to solve the problem of imbalanced storage formalized in this paper. Compared with our previous algorithm fast matching algorithm (FMA), experimental results demonstrate that HMA achieves better performance in terms of accuracy, computation overhead and storage overhead. Specifically, the computation overhead of HMA is lower than that of FMA by 84.45% on average, whereas the storage overhead of HMA is lower than that of FMA by 32.26% on average. By using the initial solution of HMA, TSA achieves the highest accuracy among GA, TSA and moth-flame optimization (MFO). Meanwhile, by using the initial solution of FMA, TSA achieves the highest accuracy among GA, TSA and MFO. [ABSTRACT FROM AUTHOR]

As an algorithmic framework, message passing is extremely powerful and has wide applications in the context of different disciplines including communications, coding theory, statistics, signal processing, artificial intelligence and combinatorial optimization. In this paper, we investigate the performance of a message-passing algorithm called min-sum belief propagation (BP) for the vertex-disjoint shortest $k$ -path problem ($k$ -VDSP) on weighted directed graphs, and derive the iterative message-passing update rules. As the main result of this paper, we prove that for a weighted directed graph $G$ of order $n$ , BP algorithm converges to the unique optimal solution of $k$ -VDSP on $G$ within $O(n^{2}w_{max})$ iterations, provided that the weight $w_{e}$ is nonnegative integral for each arc $e\in E(G)$ , where $w_{max}=\max \{w_{e}: e\in E(G)\}$. To the best of our knowledge, this is the first instance where BP algorithm is proved correct for NP-hard problems. Additionally, we establish the extensions of $k$ -VDSP to the case of multiple sources or sinks. [ABSTRACT FROM AUTHOR]

This paper focuses on the influence maximization problem in social networks, which aims to find some influence nodes that maximize the spread of information. Most existing achievements usually adopt a uniform propagation probability, without considering the topic information. Moreover, the classic Independent Cascade Model and its approximations have suffered from much running time. To overcome this limitation, this paper proposed a Topic based Shortest Path Set algorithm (TSPS). Additionally, a comprehensive set of experiments are conducted on large real-world networks, showing that our proposal provides more impressive results in the aspects of influence spread and running time. [ABSTRACT FROM AUTHOR]

A good stacking method can increase the packaging utility rate and reduce production costs. Much research has focused on 2D arrangements for rectangular, circular, or irregular shapes and regularly shaped 3D objects such as rectangular boxes. Genetic algorithms, simulated annealing, and other heuristic algorithms have been proposed. Recent research on the stacking of irregular-shaped 3D stone pieces has focused on balancing one stone piece on top of others to form one or more vertical towers, given the geometry of the stone pieces and the number of stone pieces available for the task. Stacking irregular-shaped 3D objects in a package is common in industry. However, there has been relatively little emphasis on the development of algorithms for stacking irregular-shaped 3D objects in a fixed-size container without prior knowledge of the stone geometries and the number of pieces available, with the goal of packing as many stone pieces as possible while maintaining stability. In this paper, three heuristic algorithms are proposed to solve the problem of nesting irregularly shaped stone pieces in layers within a container. All three algorithms use the following approach: (1) approximate the alignment of irregular shapes to a cluster of straight lines; (2) arrange stones one by one at the approximated angles using a step-by-step process; (3) for stability, consider the weight of the stone pieces based on pixel calculations. An automated real-time stacking system—including sensors, pneumatic suction cups, webcams, conveyor, robot, and programmable logic controller—was developed to evaluate the proposed algorithms using space utilization, stability, and cycle time as measures of performance. The developed algorithms and an existing stacking algorithm (bottom left most, or BLM) were tested using 25 sequences of 30 randomly ordered stone pieces. Results suggest that the developed algorithms effectively solve the stone piece packing problem. All three were significantly better than the BLM algorithm in terms of space utilization and stability, and there was no difference in cycle time. Algorithm 3 was better than Algorithms 1 and 2. [ABSTRACT FROM AUTHOR]

In the field of visual simultaneous localization and mapping ( VSLAM ), the key-frame selection method is mostly heuristic threshold method. This method is prone to the problem of low positioning accuracy. This paper proposed the ORB-SLAM3 algorithm based on improved key-frame selection in response to this problem. Starting from the consideration of key-frame quality, the algorithm adopted the following three standards instead of the heuristic threshold method in ORBSLAM3. The first was an adaptive key-frame threshold based on geometric constraints to ensure a sufficient number of feature points. The second was the distribution standard based on the principle of image centroid to ensure that the feature points were evenly distributed. The last was the inertial measurement unit (IMU) acceleration state observation. Experimental results on the EuRoc dataset show that the improved algorithm improves the positioning accuracy of ORB-SLAM3 by 9% in monocular inertial and 6% in stereo inertial without using the heuristic threshold. This algorithm shows better robustness and composition ability than ORB-SLAM3. [ABSTRACT FROM AUTHOR]

To reduce logistics scheduling costs and energy consumption, this paper studies the slab allocation and hot-rolling scheduling integrated optimization problem that arises in practical iron and steel enterprises. In this problem, slabs are first allocated to orders and then sent to heating furnaces for heating; then, they are sent to a hot-rolling mill for rolling. A 0–1 integer programming model is established to minimize the attribute difference in the allocation cost between slabs and orders, the switching cost of hot-rolling processing, and waiting times after slabs reach rolling mills. Given the problem's characteristics, an improved differential evolution algorithm using a real-number coding method is designed to solve it. Three different heuristic algorithms are proposed to improve the quality of solutions in the initial population. Multiple parent individuals participate in the mutation operation, which increases the population diversity and prevents the algorithm from falling into the local optimum prematurely. Experiments on 14 sets of real production data from a large domestic iron and steel plant show that our improved differential evolution algorithm generates significantly better solutions in a reasonable amount of time compared with CPLEX, the simulated artificial method, and the classical differential evolution algorithm, and it can be used by practitioners. [ABSTRACT FROM AUTHOR]

With the rise of 5G/6G and cloud computing, cluster management has become increasingly popular. Elastic cluster resources allow cloud clients to dynamically scale their resource requirements over time. Existing researches of cluster schedulers focus on improving resource scheduling speed, increasing cluster utilization, compacting the number of active physical machines (PMs) and time satisfaction function (TSF) within a cluster. The TSF is applied as a time to measure the parallel-VM scheduling problem. However, completing execution time (makespan) of task requests is often neglected, which results in inaccurate scheduling and unreasonable total cost computation. The total cost involves PM cost, migrate cost, and balance cost. To solve the problem of inaccurate scheduling of task requests and total cost billing in cluster management, in this paper, we propose an innovative heuristic algorithm, namely, multi-objective two-stage variable neighborhood searching (MO_STVNS), which aims at minimizing total cost while also considering TSF for active PMs. Moreover, we design a Multi-Objective FreeVM (MO-FreeVM) scheduler based on resource prediction, which incorporates a variety of algorithms to work in collaboration to provide near-optimal resource management for cluster. We evaluate MO_STVNS in different real traces and measure it through extensive experiments. The experimental results show that compared with state-of-art methods, the average total cost and average TSF of MO_STVNS are reduced by 33.75% and 60.67% respectively. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of social networks, the scale of social networks has grown dramatically. The relationship of people in social networks plays a key role in information propagation. Due to its huge social value, the issue of influence maximization has attracted more and more researchers' attention. With the increase of network scale, the running time has become a key issue. To resolve it, many heuristic algorithms for the influence maximization problem has been proposed. These algorithms have been verified to be be very efficient, but their effectiveness cannot be guaranteed for many networks in the real word. Therefore, it has become highly urgent to choose an influence maximization algorithm that is simultaneously efficient and effective. This paper proposes an algorithm based on the greedy algorithm framework, which converts the influence maximization problem into a maximum inner product search problem. This method can complete the selection of seed nodes for large-scale networks in nearly linear time. Compared with other algorithms, this algorithm has more application value. [ABSTRACT FROM AUTHOR]

Feature selection has become popular in data mining tasks currently for its ability of improving the performance of the algorithm and gaining more information about the dataset. Although the firefly algorithm is a well-performed heuristic algorithm, there is still much room for improvement as to the feature selection problem. In this research, an improved firefly algorithm designed for feature selection with the ReliefF-based initialization method and the weighted voting mechanism is proposed. First of all, a feature grouping initialization method that combines the results of the ReliefF algorithm and the cosine similarity is designed to take place of random initialization. Then, the direction of the firefly is modified to move toward the optimal solution. Finally, inspired by the ensemble algorithm, a weighted voter is proposed to build recommended positions for fireflies, which is also integrated with the elite crossover operator and the mutation operator to improve the diversity of the population. Selected from the mixed swarm, a new population is constructed to replace the original population in the next stage. To verify the effectiveness of the algorithm proposed in this paper, 18 datasets are utilized and 9 comparison algorithms (e.g., Black Hole Algorithm, Grey Wolf Optimizer and Pigeon Inspired Optimizer) from state-of-the-art related works are selected for the simulating experiments. The experimental results demonstrate the superiority of the proposed algorithm applied to the feature selection problem. [ABSTRACT FROM AUTHOR]

Wireless Rechargeable Sensor Networks based on wireless energy transmission successfully solve the problem of node death caused by energy shortage in traditional wireless sensor networks. Existing studies on joint energy replenishment and data collection have planned charging path first and then perform data collection. This approach does not effectively extend network life and improve network performance in data collection. In this paper, we jointly consider the impact of energy replenishment and data collection on network performance and use the Least Squares Support Vector Machine based regression prediction with dynamically changing energy consumption rate and data generation rate of sensor nodes. To solve the above problem, we propose a multi-objective path planning model for joint energy replenishment and data collection with the optimization objectives of maximizing the remaining lifetime of sensor nodes, maximizing the data collection of MC, and minimizing the amount of data loss. The conventional exact algorithm is difficult to solve the path planning problem, so heuristic algorithms have gradually become the main algorithm to solve the problem. Based on discrete fireworks algorithm and co-evolutionary algorithm, we present a grid-based multi-objective cooperation fireworks algorithm. Simulation results show that the proposed algorithm performs better in convergence and diversity. In terms of the average remaining lifetime of sensor nodes, the proposed algorithm is 0.43%, 0.52%, 1.97%, and 0.81% higher than MPACO, MODFA, NSGA-II, and SPEA-II, respectively. Similarly, the target value of solutions in data collection increased by 1.87%, 1.22%, 4.49%, and 2.10%, respectively. [ABSTRACT FROM AUTHOR]

This paper describes an optimization model for an irregular train schedule. The aim is to optimize both the maximum train loading rate and the average deviation of departure intervals under time-varying passenger transport demand for an urban rail transit line in consideration of practical train operation constraints, i.e., headway, running time between stations, dwell time, and capacity. A heuristic simulated-annealing algorithm is designed to solve the optimization model, and a case study of an urban rail transit line is performed to assess its efficacy. The results show that, compared with the current regular train schedule, the total train dwell time under the optimized irregular schedule is reduced from 900 s to 848 s, and the reduction ratio for the maximum train loading rate is from 1.2% to 3.6% for different stations. When the average train departure interval is allowed to vary from 120 to 170 s, the optimized irregular schedule decreases the maximum train loading rate of the collinear and noncollinear sections by 3.21%–4.82% and 2.52%–3.64%, respectively. Sensitivity analysis is performed for a nonnegative weight coefficient, average train departure interval, and proportion of full-length and short-turn routings. The proposed approach can be used to support capacity improvement and schedule optimization for urban rail transit lines. [ABSTRACT FROM AUTHOR]

Aiming at the problem that 5G downlink multi-user multiple-input multiple-output (MIMO) system works at millimeter wave frequency, and carries out beam selection with the Sub-6 GHz system, this paper analyzed the beam selection model of millimeter wave large-scale MIMO system and Sub-6 GHz system, and obtained spatial information from Sub-6 GHz system, and applied it to millimeter wave beam selection by logarithmic weighting. On this basis, beam selection adopted the heuristic greedy algorithm, which could match the strongest beam with low complexity and general performance. In order to match all users and beams optimally, this paper proposed a beam selection algorithm based on Hungarian algorithm, which could model the beam selection as the maximum weight matching on bipartite graph. This algorithm has higher computational complexity and better performance. [ABSTRACT FROM AUTHOR]

Large-scale natural disaster or malicious attacks could cause serious damages to the communication network in smart grids. If the damaged network cannot be recovered timely, greater threat will be brought to the secure and stable operation of smart grids. In this paper, a link importance-based network recovery method for large-scale failures in smart grids has been proposed. Firstly, the link importance for the network can be obtained according to the link importance for the service type and the importance of the service type for the network; secondly, a network recovery model, which is 0–1 integer programming, has been established to recover more important communication services with limited recovery resources; finally, we propose a heuristic algorithm to solve the problem and reduce the computational expenditure. The simulation experiments are carried out to evaluate the performance of the proposed algorithm. The simulation results illustrate that our network recovery method is applicable to the large-scale failures and the link which carries the high-priority service can be first recovered in this paper, thus which further ensures the safe and stable operation of smart grids. [ABSTRACT FROM AUTHOR]

The knapsack problem is one of the combinational optimization issues. This problem is an NP-hard problem. Soft computing methods, including the use of metaheuristic algorithms, are one way to deal with these types of problems. The standard Giza Pyramids Construction (GPC) algorithm is the first ancient-inspired algorithm that is published recently. In this paper, a binary version of the GPC algorithm for solving the 0-1 knapsack problem is proposed. For this purpose, this study uses both accumulative and multiplicative penalty functions as the objective function to determine infeasible solutions. To compare the performance, thirty different datasets have been created and the proposed algorithm has been compared with four popular and state-of-the-art algorithms. Statistical analysis has been used to find a significant difference in the performance of algorithms. The results and statistical analysis show that the proposed algorithm performs better than other metaheuristic algorithms. [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]

In recent years, the increasing use of the Internet of Things (IoT) has generated excessive amounts of data. It is difficult to manage and control the volume of data used in cloud computing, and since cloud computing has problems with latency, lack of mobility, and location knowledge, it is not suitable for IoT applications such as healthcare or vehicle systems. To overcome these problems, fog computing (FC) has been used; it consists of a set of fog devices (FDs) with heterogeneous and distributed resources that are located between the user layer and the cloud on the edge of the network. An application in FC is divided into several modules. The allocation of processing elements (PEs) to modules is a scheduling problem. In this paper, some heuristic and meta-heuristic algorithms are analyzed, and a Hyper-Heuristic Scheduling (HHS) algorithm is presented to find the best allocation with respect to low latency and energy consumption. HHS allocates PEs to modules by low-level heuristics in the training and testing phases of the input workflow. Based on simulation results and comparison of HHS with traditional, heuristic, and meta-heuristic algorithms, the proposed method has improvements in energy consumption, total execution cost, latency, and total execution time. [ABSTRACT FROM AUTHOR]

This paper addresses an integrated scheduling problem of batch manufacturing and delivery processes with a single batch machine and direct-shipping trucks. In the manufacturing process, some jobs in the same family are simultaneously processed as a production batch in a single machine. The batch production time depends only on the family type assigned to the production batch and it is dynamically adjusted by batch deterioration and rate-modifying activities. Each job after the batch manufacturing is reassigned to delivery batches. In the delivery process, each delivery batch is directly shipped to the corresponding customer. The delivery time of delivery batches is determined by the distance between the manufacturing site and customer location. The total volume of jobs in each production or delivery batch must not exceed the machine or truck capacity. The objective function is to minimize the total tardiness of jobs delivered to customers with different due dates. To solve the problem, a mixed-integer linear programming model to find the optimal solution for small problem instances is formulated and meta-heuristic algorithms to find effective solutions for large problem instances are presented. Sensitivity analyses are conducted to find the effect of problem parameters on the manufacturing and delivery time. [ABSTRACT FROM AUTHOR]

In many cities and regions, decision makers independently develop Train Operation Plan (TOP) for each line in the rail transit network, resulting in a lack of TOP Synchronization (TOPS). Considering the entire network as a whole, researchers have realized that synchronous optimization is of great significance. In this paper, we formulate two Mixed-Integer Linear Programming (MILP) models to optimize demand-driven TOP in the network. The former is an Asynchronous TOP Optimization (ATOPO) model, while the latter is a Synchronous TOP Optimization (STOPO) model. The bi-objective models simultaneously determine train frequency, train timetable, and rolling stock circulation under small-granularity passenger demand to minimize trains' total cost and passengers' total time. Then, we propose the Nondominated Sorting Coevolutionary Memetic Algorithm (NSCMA) to solve the combinatorial optimization problems. The hybrid heuristic algorithm incorporates Coevolutionary Memetic Algorithm (CMA) into Advanced and Adaptive Nondominated Sorting Genetic Algorithm II (AANSGA-II) to ameliorate the evolution process for elite individuals. On this basis, we study the case of Shenyang Metro to verify the models and the algorithm. The results demonstrate that the STOPO model is better than the ATOPO model in reducing trains' total cost and passengers' total time. In addition, NSCMA is better than AANSGA-II in obtaining elite individuals. [ABSTRACT FROM AUTHOR]

As pairwise ranking becomes broadly employed for elections, sports competitions, recommendation, information retrieval and so on, attackers have strong motivation and incentives to manipulate or disrupt the ranking list. They could inject malicious comparisons into the training data to fool the target ranking algorithm. Such a technique is called “poisoning attack” in regression and classification tasks. In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attack on the pairwise ranking algorithms, which can be generally formalized as the dynamic and static games between the ranker and the attacker, and can be modeled as certain kinds of integer programming problems mathematically. To break the computational hurdle of the underlying integer programming problems, we reformulate them into the distributionally robust optimization (DRO) problems, which are computational tractable. Based on such DRO formulations, we propose two efficient poisoning attack algorithms and establish the associated theoretical guarantees including the existence of Nash equilibrium and the generalization ability bounds. The effectiveness of the suggested poisoning attack strategies is demonstrated by a series of toy simulations and several real data experiments. These experimental results show that the proposed methods can significantly reduce the performance of the ranker in the sense that the correlation between the true ranking list and the aggregated results with toxic data can be decreased dramatically. [ABSTRACT FROM AUTHOR]

Subgraph queries are routinely used to search for entities in richly attributed graphs e.g., social networks and knowledge graphs. With little knowledge of underlying data, users often need to rewrite queries multiple times to reach desirable answers. Why-questions are studied to clarify missing or unexpected query results. This paper makes a first step to answer Why-questions for entity search in attributed graphs. We consider three common types of Why-questions: Why-not, Why, and Why-rank, which suggest query manipulations that are responsible for user-specified missing, unexpected, and undesirably ranked entities, respectively. (1) We approach a general query rewriting paradigm that suggests to identify desired entities that are specified by Why-questions. We introduce measures that characterize good query rewrites by incorporating both query editing cost and answer closeness. (2) While computing optimal query rewrites is intractable, we develop feasible algorithms, from approximation to fast heuristics, and provide query rewrites with (near) optimality guarantees whenever possible, for Why, Why-not and Why-rank questions. We further show that our results remain intact for Why questions that (1) request a single query rewrite to clarify multiple types of entities, and (2) variants such as Why-empty and Why-so-many, by providing the matching algorithms. Using real-world graphs, we experimentally verify that our algorithms are effective and feasible for large graphs. Our case study also verifies their application in e.g., knowledge exploration. [ABSTRACT FROM AUTHOR]

The variety of pricing models offered by cloud service providers and the availability of a wide diversity of computing resources has increased the popularity of this paradigm for scientific applications. Such a scalable platform can be an ideal option for the execution of loosely coupled parallel applications, such as scientific workflows. Scientific workflows are regarded as one of the most important elements in different scientific fields in which a complex application may be divided into several dependent tasks. Given that the cost of leasing multicore VMs on the cloud will rise with an increase in the number of processing cores, an efficient scheduling algorithm focusing on utilizing multicore resources can significantly reduce execution costs. As an extension of its authors' previous research, the current paper proposes a heuristic scheduling algorithm, the Cluster Dividing Algorithm that concentrates on expanding the utilization of multicore resources to reduce execution costs while also meeting the user-defined deadline. To increase resource utilization, the proposed scheduling employs different techniques, such as task clustering, directed graph leveling, and task duplicating. The experimental results reveal that the presented algorithm leads to lower execution costs while complying with the deadline. [ABSTRACT FROM AUTHOR]

The operation of the photovoltaic (PV) system under partial shading conditions (PSC) is complicated since the output characteristic of the PV system is profoundly affected by the heterogeneous irradiance of PSC. This paper proposes a dynamic reconfiguration framework to tackle PSC in the PV array. Continuous operation of the dynamic PV array reconfiguration under cloud-induced partial shading is considered by developing an emulator of the moving cloud. In addition, the Particle Swarm Optimization and Rao algorithms are improved to obtain the optimal PV array configuration under PSC. The operation of switching is enhanced by simultaneously considering the total switching times and the operation of highly active switches. The simulation results on the 9 × 9 PV array demonstrate the effectiveness of the proposed framework in terms of reducing the number of local maximum power points on the power-voltage characteristic, enhancing power output, and relieving stress on the switching operation of the PV array under different PSC. [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]