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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]

Key of development for OLED devices is compatibility between organic materials. Therefore, the construction of the materials' properties DB should be followed in order to advance characterization and prediction of the OLED device. In particular, it is necessary to build a number of organic materials DB unlike the conventional Si‐based homo‐junction and alloy changing only heterojunction. So, it should be built based on the organic material parameters, SIMDB (Simulation DB), it is possible to predict and optimize driving voltage and efficiency characteristics for OLED devices. However, the skilled Engineer gets to decide on the simple repeat operation and time consumption for calibrating the material parameter/structure (4~5 layer device based on 20,000 times or more). In order to solve such problems, this paper presents an automatic optimization for electro‐optical characteristics of OLED device. The proposed method and tool are based on a unique heuristic algorithm, which is obtained from accumulated manual calibration experience. In other words, the convergence know‐how is applied random search algorithm and heuristic algorithm were constructed in‐house optimizer enables automated extraction of OLED properties. Finally, by utilizing the built SIMDB is expected to various factors affecting the OLED elements is optimized to be analyzed and prior prediction. [ABSTRACT FROM AUTHOR]

To improve cargo loading efficiency and achieve diverse needs of companies for the loading process, this paper innovatively establishes a multiple objective mixed integer programming model for the three-dimensional multiple bin-size bin packing problem (3D-MBSBPP). The model is designed to maximize container space utilization rate and cargo load balance, minimize container usage costs, and incorporates some practical constraints. On this basis, we propose a novel dynamic feedback algorithm based on spatial corner fitness (SCF_DFA) to solve this model, which consists of three stages. Specifically, Stage 1 employs a heuristic algorithm based on spatial corner fitness to optimize the search of the remaining spaces. Stage 2 employs a container type selection algorithm to dynamically adjust and optimize container types. Stage 3 uses an improved genetic algorithm to improve the quality of the solutions of the first two stages. We demonstrate the effectiveness of the proposed algorithm through comparative experiments on benchmark instances, and apply it to solve the real-life instances for the 3D-MBSBPP. The results show that the proposed algorithm can make the average container space utilization rate reach 85.38%, which is 1.48% higher than that of baseline method, while the loading results obtained are more balanced, indicating the advantages of the SCF_DFA in solving 3D-MBSBPP. Furthermore, we conduct ablation experiments to confirm the effectiveness of each component within the algorithm. [ABSTRACT FROM AUTHOR]

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]

The real-time path planning of unmanned aerial vehicles (UAVs) in dynamic environments with moving threats is a difficult problem. To solve this problem, this paper proposes a time-based rapidly exploring random tree (time-based RRT*) algorithm, called the hierarchical rapidly exploring random tree algorithm based on potential function lazy planning and low-cost optimization (HPO-RRT*). The HPO-RRT* algorithm can guarantee path homotopy optimality and high planning efficiency. This algorithm uses a hierarchical architecture comprising a UAV perception system, path planner, and path optimizer. After the UAV perception system predicts moving threats and updates world information, the path planner obtains the heuristic path. First, the path planner uses the bias sampling method based on the artificial potential field function proposed in this paper to guide sampling to improve the efficiency and quality of sampling. Then, the tree is efficiently extended by the improved time-based lazy collision checking RRT* algorithm to obtain the heuristic path. Finally, a low-cost path optimizer quickly optimizes the heuristic path directly to optimize the path while avoiding additional calculations. Simulation results show that the proposed algorithm outperforms the three existing advanced algorithms in terms of addressing the real-time path-planning problem of UAVs in a dynamic environment. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) equipped with communication technologies have gained significant attention as a promising solution for providing wireless connectivity in remote, disaster-stricken areas lacking communication infrastructure. However, enabling UAVs to provide communications (e.g., UAVs acting as flying base stations) in real scenarios requires the integration of various technologies and algorithms. In particular 3D path planning algorithms are crucial in determining the optimal path free of obstacles so that UAVs in isolation or forming networks can provide wireless coverage in a specific region. Considering that most of the existing proposals in the literature only address path planning in a 2D environment, this paper systematically studies existing path-planning solutions in UAVs in a 3D environment in which optimization models (optimal and heuristics) have been applied. This paper analyzes 37 articles selected from 631 documents from a search in the Scopus database. This paper also presents an overview of UAV-enabled communications systems, the research questions, and the methodology for the systematic mapping study. In the end, this paper provides information about the objectives to be minimized or maximized, the optimization variables used, and the algorithmic strategies employed to solve the 3D path planning problem. [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]

Inferring multilayer diffusion networks from observed cascades is both crucial and realistic. To infer multilayer diffusion networks, constructing continuous-time diffusion models that capture diffusion dynamics is a prerequisite. However, developing such models faces two main challenges: (1) reducing the number of learnable parameters for precise optimization with limited cascades while effectively modeling for accurate inference and (2) adapting the models to more realistic scenarios. In this paper, we propose a novel continuous-time diffusion model, namely the Embedding-based Continuous-time Diffusion (ECD) model, which employs an embedding method while modeling symmetric relationship strength, asymmetric relationship strength, and trust strength. Specifically, by leveraging the embedding method, the number of learnable parameters is significantly reduced compared with previous models. Then, by modeling symmetric relationship strength, our model can be used in scenarios where the relationships between nodes are symmetric. Subsequently, the trust strength can be inferred by our proposed efficient heuristic algorithm, making our model suitable for scenarios where time information is unavailable. Furthermore, we develop an optimization algorithm to optimize the proposed model and infer multilayer diffusion networks. The experimental results on synthetic and real datasets show that our model and algorithms outperform the comparison methods. [ABSTRACT FROM AUTHOR]

This paper studies the single-machine scheduling problem with truncated learning effect, time-dependent processing time, and past-sequence-dependent delivery time. The delivery time is the time that the job is delivered to the customer after processing is complete. The goal is to determine an optimal job schedule to minimize the total weighted completion time and maximum tardiness. In order to solve the general situation of the problem, we propose a branch-and-bound algorithm and other heuristic algorithms. Computational experiments also prove the effectiveness of the given algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a heuristic association algorithm between access points (APs) and user equipment (UE) in user-centric cell-free massive multiple-input-multiple-output (MIMO) systems, specifically targeting scenarios where UEs share the same frequency and time resources. The proposed algorithm prevents overserving APs and ensures the connectivity of all UEs, even when the number of UEs is significantly greater than the number of APs. Additionally, we assume the use of low-resolution analog-to-digital converters (ADCs) to reduce fronthaul capacity. While realistic massive access scenarios, such as those in Internet-of-Things (IoT) environments, often involve hundreds or thousands of UEs per AP using multiple access techniques to allocate different frequency and time resources, our study focuses on scenarios where UEs within each AP cluster share the same frequency and time resources to highlight the impact of pilot contamination in dense network environments. The proposed algorithm is validated through simulations, confirming that it guarantees the connection of all UEs and prevents overserving APs. Furthermore, we analyze the required fronthaul capacity based on quantization bits and confirm that the proposed algorithm outperforms existing algorithms in terms of SE and average SE performance for UEs. [ABSTRACT FROM AUTHOR]

With the rapid development of industrial IoT technology, a growing number of intelligent devices are being deployed in smart factories to digitally upgrade the manufacturing industry. The increasing number of intelligent devices brings a huge task request. Fog computing, which is an emerging distributed computing paradigm, is widely applied to process the device data generated in smart manufacturing. However, as fog nodes are resource limited and geographically widely distributed limitations, proper fog node placement strategies are critical to enhance the service performance of fog computing systems. In this paper, we study the problem of fog node placement in smart factories and divide it into two scenarios, fixed device and mobile device fog node placement, depending on the mobility of the devices. The fog node placement model and objective function are built in the two scenarios, and two improved heuristic algorithms are proposed to obtain the most optimal placement scheme. In addition, we perform simulation experiments based on existing intelligent production line prototype platforms and devices to evaluate the performance of the proposed algorithms. The IGA reduces latency by an average of 586.7 - 1089 ms over the benchmark algorithm, saving 18.3 - 39 % in energy consumption. The total latency of IMOA is reduced by 59.8 - 68.5 % , and the maximum latency is reduced by 48.8 - 69.2 % . The experimental results show that the proposed algorithms outperform other benchmark algorithms in terms of task response time and energy consumption. [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]

This paper focuses on minimum-time path tracking, a subproblem in motion planning of robotic systems. We generate a time-optimal velocity profile for robotic manipulators taking into account kinematic and dynamic constraints. Based on the special structure of the constraints (called peaked constraints), profile generation is formulated as a linear programming (LP) problem. The LP-based control problem is solved by a sequential optimization method. The presented algorithm has reduced computational time compared to a general LP solver. [ABSTRACT FROM AUTHOR]

Artificial physical optimizer (APO), as a new heuristic stochastic algorithm, is difficult to balance convergence and diversity when dealing with complex multi-objective problems. This paper introduces the advantages of R2 indicator and target space decomposition strategy, and constructs the candidate solution of external archive pruning technology selection based on APO algorithm. A hybrid strategy guided multi-objective artificial physical optimizer algorithm (HSGMOAPO) is proposed. Firstly, R2 indicator is used to select the candidate solutions that have great influence on the convergence of the whole algorithm. Secondly, the target space decomposition strategy is used to select the remaining solutions to improve the diversity of the algorithm. Finally, the restriction processing method is used to improve the ability to avoid local optimization. In order to verify the comprehensive ability of HSGMOAPO algorithm in solving multi-objective problems, five comparison algorithms were evaluated experimentally on standard test problems and practical problems. The results show that HSGMOAPO algorithm has good convergence and diversity in solving multi-objective problems, and has the potential to solve practical problems. [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 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]

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]

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]

Service-oriented architectures have become increasingly prevalent in internet application design, leading to a proliferation of services. Managing and composing these web services pose a classic non-deterministic polynomial-time hard problem. The web service composition problem (WSCP) based on the input–output model (IOM) involves the automatic composition of services without relying on a pre-defined workflow. Multi-objective evolutionary algorithms offer a solution that goes beyond simple weighted average calculations for quality of service, catering to diverse user needs in service composition. This paper introduces a multi-objective heuristic algorithm based on the SPEA2 called MSPEA2+. Specific modifications are incorporated in MSPEA2+ to support the breeding, crossover, and mutation processes, tailored for IOM-based WSCP. Validating the algorithm performance through finding suitable combination results for each task in the WSC-08 dataset, our experiments showed that MSPEA2+ has better iterative efficiency than existing multi-objective methods and outperforms them in terms of quality of service. MSPEA2+ achieved the lowest IGD value across all seven tasks, an improvement of 81.7% IGD value compared with the SPEA2 algorithm in WSC-0804. [ABSTRACT FROM AUTHOR]

The goal of the influence maximization problem is to identify a seed set that maximizes the number of users who will be impacted. This issue is critical to the spread of information in social networks. The greedy and heuristic algorithms are two prominent algorithms for this problem. The greedy algorithms are usually effective but they have high computational complexity. The heuristic algorithms are tendly efficient while the accuracy is low. The major goal of this study is to strike a compromise between efficacy and efficiency in selecting k influential nodes to maximize influence propagation while keeping the expense of doing so to a minimal. This paper proposes an improved heuristic algorithm, called the Best Hop for Independent Cascade Model (BHICM). This approach presents a dynamic relationship strategy between propagation probability and hop count that avoids processing the seed node's neighbors. Afterwards, the proposed algorithm defines the diffusion score as the criterion for selecting seed nodes, which ensures its accuracy and efficiency. According to experimental results on four genuine social networks, the proposed approach exceeds all comparison algorithms in terms of accuracy while maintaining an acceptable running time. [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 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]

In this paper, we consider the problem of learning undirected graphical models from data generated according to the Glauber dynamics (also known as the Gibbs sampler). The Glauber dynamics is a Markov chain that sequentially updates individual nodes (variables) in a graphical model and it is frequently used to sample from the stationary distribution (to which it converges given sufficient time). Additionally, the Glauber dynamics is a natural dynamical model in a variety of settings. This paper deviates from the standard formulation of graphical model learning in the literature, where one assumes access to independent identically distributed samples from the distribution. Much of the research on graphical model learning has been directed toward finding algorithms with low computational cost. As the main result of this paper, we establish that the problem of reconstructing binary pairwise graphical models is computationally tractable when we observe the Glauber dynamics. Specifically, we show that a binary pairwise graphical model on p nodes with maximum degree d can be learned in time f(d)p^2\log p , for a function f(d) defined explicitly in this paper, using nearly the information-theoretic minimum number of samples. [ABSTRACT FROM AUTHOR]

Reviewers play a significant role in academic peer review activities, including conference paper assignment and funding selection, because their evaluation of proposals impacts the final decision. Several studies have proposed reviewer selection strategies or reviewer evaluation methods for solving the problem of selecting appropriate reviewers. Identifying reviewers who are familiar with the proposals to be reviewed is the objective of the reviewer assignment problem. However, the majority of the existing studies ignore quantitative constraints with respect to the articles assigned to the reviewers during the review process. In this study, we propose a novel optimization model with several review condition constraints to address the reviewer assignment problem. In the proposed model, the expertise and research areas of the candidate reviewers and proposals are identified using semantic topic models, which are demonstrated to be effective when measuring the relevance of the reviewers with respect to the proposals to be reviewed; further, the computational efficiency is improved owing to the reduced representation dimensionality. Herein, an improved heuristic algorithm is proposed to match reviewers and papers based on specific topic areas, and candidate reviewers are assigned to each proposal under the global optimum condition based on their overall performance values. Subsequently, an empirical test is conducted using a conference reviewer dataset. The obtained results show that the proposed model can help the managers to efficiently and effectively select reviewers in terms of the convergence rate and convergence level when compared with several classic benchmarks. • A novel optimization model is proposed to address the reviewer assignment problem. • The expertise and profiles are identified with the semantic topic models. • An improved heuristic algorithm is proposed to assign reviewer with global optimum. • The effectiveness and robustness is validated in three stages of experiments. [ABSTRACT FROM AUTHOR]

[Objective] Trajectory planning is critical for the motion control of a manipulator. The motion time and joint vibration of the manipulator can be reduced by providing a fast and stable reference trajectory to the controller. The single objective trajectory optimization algorithm remains insufficient in meeting the increasing production needs; therefore, it is often necessary to use a multiobjective heuristic algorithm to optimize the motion trajectory of the manipulator while satisfying its kinematic constraints to optimize the motion time and reduce the joint impact or energy loss, improving the production efficiency of the manipulator and ensuring its stability during motion. An improved adaptive multiobjective particle swarm optimization (IAMOPSO) method is studied for the time- and impact-based optimal trajectory planning of a multidegree-of-freedom manipulator. [Methods] First, to obtain physically continuous motion trajectories, including position, velocity, and acceleration, we use a quintic B-spline curve to interpolate the joint path points based on the local controllability of the curve. The trajectory optimization process is a step-by-step approach so that the interpolated solution optimally satisfies the kinematic constraints of the manipulator, and the B-spline curve passes through all interpolation points, rendering the motion trajectory of the manipulator more adaptable. Second, a specific objective function improves the motion efficiency of the manipulator. Under certain kinematic constraints, an expression for the joint impact of the manipulator is designed to improve the trajectory tracking accuracy and ensure the stability of the manipulator during operation tasks. Finally, to increase the diversity of the nondominated solution set while avoiding the local extremum, the multi-objective particle swarm optimization (MOPSO) algorithm is improved through a hybrid strategy of mutation operators, adaptive weights, and dynamic learning factors; consequently, the objective function is optimally solved, and the average optimal solution is selected using a normalization function. A nonlinear mutation operator is adopted to encourage particles to comprehensively explore the decision space, ensuring population diversity in the initial iteration of the algorithm, and an adaptive weight strategy and dynamic learning factor are adopted to balance the exploration and development of the MOPSO algorithm, making it easy for the algorithm to determine the global optimal solution. [Results] 1 The quintic B-spline curve accurately interpolates the path points and ensures the continuity of the acceleration and jerk curves, thus meeting the requirements of smoothness and continuity of the joint trajectory of the manipulator; 2 the optimal trajectory planning method based on the IAMOPSO algorithm can yield a Pareto front with good convergence, select the time- and impact-based optimal trajectory according to the average optimal criterion, and achieve continuity of joint angles and their derivatives. [Conclusions] The optimal trajectory planning method for the manipulator based on IAMOPSO proposed in this paper can improve the motion efficiency and tracking accuracy of the manipulator, obtain Pareto front surfaces with good convergence, ensure smooth and continuous joint curves of the manipulator, and improve the operational efficiency and stability of the manipulator. [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]

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]

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]

This paper investigates the dynamic access point (AP) clustering and precoding problem in the downlink of user-centric virtual cell networks. The goal is to maximize the weighted sum spectral efficiency (SE) while satisfying the power constraints and AP clustering constraints in adjacent time slots (TSs). By adopting the random walk mobility to model the mobile user equipments’ movement behaviors, we consider dynamic and time-varying channel conditions. Therefore, the weighted sum SE maximization programming takes the form of discrete-time sequence of mixed-integer non-convex optimization problems. In this paper, we propose to solve this sequential problem in two stages. In the first stage, a dynamic AP clustering approach based on discrete particle swarm optimization is developed. This approach takes the advantage of the channel correlation by exploiting the relationship between AP clustering solutions in adjacent TSs to improve the SE performance and reduce complexity. In the second stage, given the AP clustering solution obtained in the first stage, a distributed precoding algorithm is devised via applying the weighted minimum mean square error method. By combining these two stages, we propose a novel dynamic AP clustering and precoding algorithm (DAPC-Pre). The effectiveness of the proposed DAPC-Pre algorithm is verified by the simulation results. In particular, the proposed algorithm converges fast and significantly outperforms benchmark algorithms in terms of sum SE under different dynamic environments. [ABSTRACT FROM AUTHOR]

Many big data applications produce a massive amount of high-dimensional, real-time, and evolving streaming data. Clustering such data streams with both effectiveness and efficiency are critical for these applications. Although there are well-known data stream clustering algorithms that are based on the popular online-offline framework, these algorithms still face some major challenges. Several critical questions are still not answer satisfactorily: How to perform dimensionality reduction effectively and efficiently in the online dynamic environment? How to enable the clustering algorithm to achieve complete real-time online processing? How to make algorithm parameters learn in a self-supervised or self-adaptive manner to cope with high-speed evolving streams? In this paper, we focus on tackling these challenges by proposing a fully online data stream clustering algorithm (called ESA-Stream) that can learn parameters online dynamically in a self-adaptive manner, speedup dimensionality reduction, and cluster data streams effectively and efficiently in an online and dynamic environment. Experiments on a wide range of synthetic and real-world data streams show that ESA-Stream outperforms state-of-the-art baselines considerably in both effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile Internet, high-definition video and cloud computing, users' bandwidth demands are not only larger and larger but also more and more diverse. To solve this problem, there searchers put forward the concept of elastic optical network (EON). EON adopts the transmission mode of elastic grid, which can allocate spectrum resources flexibly and meet high bandwidth and diversity requirements at the same time. Routing and spectrum allocation (RSA) is an important issue in EON. In this paper, we present a heuristic algorithm named constrained-lower-indexed-block (CLIB) allocation algorithm for the RSA problem. The algorithm is based on the K candidate paths. When there are available spectrum blocks on multiple candidate paths, if the increase of the path length does not exceed a given threshold, the lower index spectrum would be selected for the connection request on a longer path. The aim of the algorithm is to concentrate the occupied frequency slices on one side of the spectrum and leave another side of the spectrum to the later arrived connection requests as much as possible, to reduce the blocking probability of connection requests. Simulation results show that comparing with the first-last-fit and hybrid grouping algorithms, the CLIB algorithm can reduce the blocking probability of connection requests. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing (MCS) has been an effective sensing paradigm by exploiting the pervasive sensor-rich mobile devices for sensor data collection. Online task assignment is an important issue for mobile crowdsensing since tasks typically arrive dynamically and need to be handled in an online manner. In this paper, we study online task assignment for maximizing the total profit of the MCS platform while satisfying the time window requirement of each task. We first describe the crowdsensing model and then study the online task assignment in the following two different scenarios: (1) user-offline-arriving scenario, where all users are fully available throughout the whole sensing period and their movements are fully planned by the platform; (2) user-online-arriving scenario, where users arrive and depart dynamically and each user has a specific participatory time window for task executions. For the former scenario, we propose a benchmark algorithm and also an online heuristic algorithm. The benchmark algorithm tries to provide a best-case performance by assuming all future task arrival information is known in advance. The online algorithm adopts bipartite-matching-based strategy for task assignment and further performs minimal detour based data offloading for reducing the data upload cost, whenever possible. For the latter scenario, we propose an effective online algorithm, which adopts a maximum-profit-first strategy for task assignment and also minimal detour based data offloading for reduction of data upload cost whenever applicable. For all the proposed algorithms, we present their detailed design and deduce their time complexities. Extensive simulations are conducted and the results demonstrate that our proposed algorithms can largely increase the total profit of the platform as compared with existing work. [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]

Summary: Heuristic algorithms are widely used to solve multi‐objective test case prioritization (MOTCP) problems. However, they perform differently for different test scenarios, which conducts difficulty in applying a suitable algorithm for new test requests in the industry. A concrete hyper‐heuristic framework for MOTCP (HH‐MOTCP) is proposed for addressing this problem. It mainly has two parts: low‐level encapsulating various algorithms and hyper‐level including an evaluation and selection mechanism that dynamically selects low‐level algorithms. This framework performs good but still difficult to keep in the best three. If the evaluation mechanism can more accurately analyse the current results, it will help the selection strategy to find more conducive algorithms for evolution. Meanwhile, if the selection strategy can find a more suitable algorithm for the next generation, the performance of the HH‐MOTCP framework will be better. In this paper, we first propose new strategies for evaluating the current generation results, then perform an extensive study on the selection strategies which decide the heuristic algorithm for the next generation. Experimental results show that the new evaluation and selection strategies proposed in this paper can make the HH‐MOTCP framework more effective and efficient, which makes it almost the best two except for one test object and ahead in about 40% of all test objects. [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]

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 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]

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]

In this paper, we present a novel design approach for a wideband non-uniformly spaced linear array (NUSLA) to suppress the effect of interference signals. Notably, a uniform linear array (ULA), which is easy to handle, is widely utilized to generate nulls over a wide band; however, its beamforming performance is limited owing to the ULA structure. Although a NUSLA with nonlinear spacing addresses nonlinear problems that are difficult to handle mathematically, it can exhibit performance improvements surpassing those of ULA structures. However, an additional constraint is required to generate nulls at a specific position, and this has not yet been studied for null generation using wideband NUSLA. In this paper, we propose a novel cost function for designing a wideband NUSLA, which generates a null at the desired position, and we utilize the modified reinforcement learning algorithm (MORELA), which is a heuristic optimization algorithm based on reinforcement learning (RL), to minimize the proposed cost function and analyze the optimized antenna array and weights. Further, we compare the performance of the proposed MORELA based on RL with that of existing heuristic optimization algorithms via computer simulations. [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 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]

Mapping optimization of network-on-chips (NoCs) for specific applications has become one of the most important keys of the SoC top-level design. However, the topology of NoC applied is usually regular topology, such as mesh, torus, etc., which may generate a large number of isomorphic solutions during the process of NoC mapping, which may reduce the convergence speed of mapping algorithms. In this paper, we proposed a generic-based hyper-heuristic algorithm named IRC-GHH for NoC mapping. To reduce the influence of isomorphic solutions, we analyzed the symmetry of NoC topology and proposed crossover operators based on the isomorphic solution to optimize the algorithm. We studied the situation of invalid crossovers and eliminated invalid iterations by adopting an isomorphic replacement crossover (IRC) strategy. To prevent the algorithm from falling into evolutionary stagnation in the late iteration, we introduce an adaptive mechanism to increase the usage frequency of the IRC operator automatically. Compared with GHH without IRC, the GHH with IRC can achieve, on average 15.25% communication energy reduction and 7.84% communication delay reduction. [ABSTRACT FROM AUTHOR]

In a human–robot coexisting environment, reaching the target place efficiently and safely is pivotal for a mobile service robot. In this paper, a Risk-based Dual-Tree Rapidly exploring Random Tree (Risk-DTRRT) algorithm is proposed for the robot motion planning in a dynamic environment, which provides a homotopy optimal trajectory on the basis of a heuristic trajectory. A dual-tree framework consisting of an RRT tree and a rewired tree is proposed for the trajectory searching. The RRT tree is a time-based tree, considering the future trajectory predictions of the pedestrians, and this tree is utilized to generate a heuristic trajectory. However, the heuristic trajectory is usually nonoptimal. Then, a line-of-sight (LoS) control checking algorithm is proposed to detect whether two time-based nodes can be rewired with the least cost. On the basis of the LoS control checking algorithm, a tree rewiring algorithm is proposed to optimize the heuristic trajectory. The tree generated in the tree rewiring process is called the rewired tree. The trajectory generated by the Risk-DTRRT algorithm proves to be optimal in the homotopy class of the heuristic trajectory. The navigation run time and the lengths of the planned trajectories are selected to demonstrate the effectiveness of the proposed algorithm. The experimental results in both simulation studies and real-world implementations reveal that our proposed method achieves convincing performance in both static and dynamic environments. Note to Practitioners—This paper is motivated by planning optimized trajectories for the mobile service robots in dynamic environments with pedestrians. In this area, the sampling-based motion planning algorithms have been widely used for their high efficiency and robustness. However, the real-time optimality of the motion planning cannot be guaranteed due to the challenges caused by the moving pedestrians. In this paper, we propose a dual-tree framework to solve this problem. First, a classic Rapidly exploring Random Tree (RRT) is constructed to generate a heuristic trajectory. Then, instead of reconnecting the nodes on the heuristic trajectory directly, a rewired tree is built to optimize the heuristic trajectory. This proposed dual-tree framework can fully exploit the information of the RRT tree and ensure the completeness of the motion planning. The proposed motion planning algorithm also considers the constraints of the nonholonomic mobile robots, and it can be applied in most mobile service robots to improve their motion planning quality. [ABSTRACT FROM AUTHOR]

With the wind power energy largely interconnected with power grid, it is necessary to develop a robust dynamic equivalent model of the wind farm for the system stability analysis and control. In this paper, a detailed wind farm model with doubly fed induction generators is established according to Western Electricity Coordinating Council benchmark test system. Then, the parameters identification process based on aggregation technique consisting of a hybrid algorithm combined improved-particle swarm optimization with improved-genetic algorithm is proposed for dynamic equivalent model development by using the measurement data from phasor measurement unit at the point of interconnection. Additionally, the robustness of the developed equivalent model is analyzed. The simulation results of the benchmark test system and IEEE-9 bus system demonstrate the effectiveness and robustness of the proposed equivalent modeling and identification methodology. [ABSTRACT FROM AUTHOR]

Segmentation is an essential processing step in computer vision. As a popular method in image segmentation, the pivotal of multilevel thresholding lies in determining specific thresholds of the image. In this paper, we introduce an enhanced Giza pyramids construction algorithm (GPC) with a gradient contour approach, termed GGPC, for solving color image segmentation problems. In the proposed GGPC algorithm, the gradient contour approach explores the fitness landscape based on population distributions to speculate promising summits, and provides an efficient guidance for further evolution. A composite suit of benchmark functions is adopted to evaluate the proposed GGPC algorithm which is shown via extensive comparisons to outperform eight state-of-the-art algorithms. To further develop the application potential, we propose an excellent GGPC-based thresholding segmentation method by multilevel Kapur entropy. The method is successfully exploited in color image segmentation with respect to different categories of benchmark images. Experiment results demonstrate that the GGPC-based segmentation method also exhibits better performance over peers, providing a more effective technique for color image segmentation. [ABSTRACT FROM AUTHOR]

Value‐at‐risk (VaR) is a widely acceptable risk measure in finance and particularly, in the portfolio selection problem (PSP). However, due to the non‐convexity, its incorporation into an optimization model makes it challenging and difficult to solve. Hence, the development of iterative‐based heuristic algorithms (IBHAs) utilizing the problem structure would be valuable. The existing IBHAs are either inefficient in terms of solution quality and running time or are just applicable to solve simple PSPs without any real‐world constraint (i.e., cardinality, position size, and transaction cost constraints). To overcome these shortcomings, in this paper, we present a novel IBHA to efficiently solve the PSP with real‐world constraints under VaR measure. Our method is based on iterative resolution of two restricted versions of the original model in which some of the binary variables utilized in the formulation of VaR measure are fixed at specific values. Computational experiments over real‐world instances indicate that not only the running time of our algorithm is short, but also its solution quality is much better than the existing IBHAs in 97% of instances with an average improvement of about 10%. [ABSTRACT FROM AUTHOR]

The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process. However, for complex and dynamic cloud service scheduling tasks, due to the difference in service attributes, the solution efficiency of a single strategy is low for such problems. In this paper, we presents a hyper-heuristic algorithm based on reinforcement learning (HHRL) to optimize the completion time of the task sequence. Firstly, In the reward table setting stage of HHRL,we introduce population diversity and integrate maximum time to comprehensively determine the task scheduling and the selection of low-level heuristic strategies. Secondly, a task computational complexity estimation method integrated with linear regression is proposed to influence task scheduling priorities. Besides, we propose a high-quality candidate solution migration method to ensure the continuity and diversity of the solving process. Compared with HHSA, ACO, GA, F-PSO, etc, HHRL can quickly obtain task complexity, select appropriate heuristic strategies for task scheduling, search for the the best makspan and have stronger disturbance detection ability for population diversity. [ABSTRACT FROM AUTHOR]

For large-scale engineering problems, it has been generally accepted that domain-partitioning algorithms are highly desirable for general-purpose finite element analysis (FEA). This paper presents a heuristic numerical algorithm that can efficiently partition any transportation network (or any finite element mesh) into a specified number of subdomains (usually depending on the number of parallel processors available on a computer), which will result in "minimising the total number of system BOUNDARY nodes" (as a primary criterion) and achieve "balancing work loads" amongst the subdomains (as a secondary criterion). The proposed seven-step heuristic algorithm (with enhancement features) is based on engineering common sense and observation. This current work has the following novelty features: (i) complicated graph theories that are NOT needed and (ii) unified treatments of transportation networks (using line elements) and finite element (FE) meshes (using triangular, tetrahedral, and brick elements) that can be performed through transforming the original network (or FE mesh) into a pseudo-transportation network which only uses line elements. Several examples, including real-life transportation networks and finite element meshes (using triangular/brick/tetrahedral elements) are used (under MATLAB computer environments) to explain, validate and compare the proposed algorithm's performance with the popular METIS software. [ABSTRACT FROM AUTHOR]