Structural balance in signed networks aims to search for the structure with the least imbalance of relationships. However, most existing studies focus on global structural balance, and little work has considered local...
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Structural balance in signed networks aims to search for the structure with the least imbalance of relationships. However, most existing studies focus on global structural balance, and little work has considered local structural balance. In this study, an optimization model is proposed based on the weak definition of structural balance theory. The model incorporates both the global imbalance and maximum cluster imbalance as the criterion of structural balance in a signed network. Furthermore, a priority strategy based memetic algorithm, called PSMA, is presented to calculate the structural balance based on this model. In PSMA, a network-specific genetic operation is applied to explore the solution space. A multi-level greedy method is deployed to exploit the optimal solution as the local search. The priority strategy, which aims to recognize the severe unbalanced vertices or clusters as prior objectives and execute search operations on them, is inserted into each level of the local search to make it more efficient. Extensive experiments are conducted on 11 real-world network datasets. The results demonstrate that the proposed model can avoid clusters with over-concentration of unbalanced relationships at the expense of a slight increase in global imbalance and confirm that the proposed PSMA can achieve better global and cluster structural balance compared with the state-of-the-art algorithms.
A vast amount of data is generated daily from all aspects of individual lives due to the increasing proliferation of devices that are connected to the internet. These internet-connected gadgets lack the necessary capa...
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A vast amount of data is generated daily from all aspects of individual lives due to the increasing proliferation of devices that are connected to the internet. These internet-connected gadgets lack the necessary capacity, resources, and storage to effectively process and retain large volumes of data with accuracy and reliability. Therefore, it is vital to regulate and process the unpredictable data created by a computing paradigm with robust resource specifications. Cloud computing has been considered an appealing solution for effectively analyzing and storing this data within a specific timeframe. Furthermore, the existence of multiple conflicting factors and the classification of the problem as NP-hard make resource management and task consolidation major obstacles in Cloud-based systems. This research suggests a hybrid Resource Monitoring, Task Scheduling and Migration technique that combines an Adaptive Neuro-Fuzzy Inference System (ANFIS) with a Binary Quantum-based Avian Navigation Optimizer technique (BQANA) to tackle these challenges. The BQANA algorithm is utilized to enhance the control parameters of the ANFIS system. Additionally, a load balancing technique is proposed to provide an even distribution of workloads among Cloud Virtual Machines (VMs) to optimize resource management. Furthermore, a task migration strategy has also been adopted to offload the overloaded tasks to under-utilized VMs. The proposed approach presented in this study has been thoroughly validated using extensive simulations on real-world benchmark datasets, specifically for Quality of Service (QoS) characteristics. The simulation results demonstrate that the proposed methodology outperforms previous methods with regard to makespan, resource utilization, response time, energy consumption, and load balancing, with respective enhancements of 24.7%, 15.4%, 16.9%, 4.51%, and 23.4%.
Autonomous underwater vehicle (AUV)-based data collection can bring significant advantages to underwater wireless sensor networks (UWSNs). Collaborative collection based on multi-AUV task allocation is an effective wa...
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Autonomous underwater vehicle (AUV)-based data collection can bring significant advantages to underwater wireless sensor networks (UWSNs). Collaborative collection based on multi-AUV task allocation is an effective way to reduce delay. However, the existing research work seldom considers the real current environment in the task allocation, which leads to the large delay and yaw of AUVs. The introduction of ocean currents makes the existing task allocation algorithms no longer applicable due to the poor solving ability and long convergence time. Therefore, we propose an efficient task allocation algorithm named genetic algorithm N-step reinforcement learning improved DQN (GA-NDQN) by combining the genetic algorithm (GA) and N-step reinforcement learning (RL) nature of DQN to minimize the data collection delay. In our work, to minimize the impact of ocean currents on the AUV's travel, the specific trajectory optimization problem between adjacent nodes is considered and modeled as a minimum weight sum problem (MWSP). To complete the entire data collection process, we performed path planning for AUVs and modeled it as an asymmetric traveling salesman problem (ATSP). A* algorithm and the Lin-Kernighan-Helsgaun (LKH) algorithm are designed to solve these problems, which are further nested in GA-NDQN to optimize the task allocation strategy for data collection. Finally, the effectiveness of the proposed scheme is verified by extensive simulation results.
This study aims to explore effective energy utilization and extension of cell operation time to ensure the continuity and reliability of network communications after disasters by proposing efficient energy management ...
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This study aims to explore effective energy utilization and extension of cell operation time to ensure the continuity and reliability of network communications after disasters by proposing efficient energy management strategies. This study presents intracluster and intercluster energy saving management (ESM) algorithms to provide a network environment with high energy efficiency and reliability. This study proposes intracluster ESM to reduce the energy consumption of small cells and extend their average operation time. The cluster-based ESM mechanism reduces the average energy consumption of small cells by about 20.2% while increasing the average operation time by 40%.
Wireless sensor networks face issues such as high energy consumption and the impact of individual node failures on the entire network. In order to effectively regulate redundant nodes to balance node coverage and ener...
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Wireless sensor networks face issues such as high energy consumption and the impact of individual node failures on the entire network. In order to effectively regulate redundant nodes to balance node coverage and energy consumption in the network, the Ditian algorithm is introduced and initialized. The computation structure is reconstructed through adaptive adjustment, and the discrimination of redundant nodes is optimized. Meanwhile, the cluster topology is added to wireless sensor networks to control network nodes and complete the information transmission process. The experimental results showed that the improved Ditian had an average remaining energy of 3.6J after 600 runs. In the validation set, the energy efficiency was further improved, with an average remaining energy of 1.92J and a standard deviation of 0.51J. When the RMSE threshold was 0.8, the network lifetime reached 11 minutes, and the running time was significantly better than comparison algorithms. Furthermore, in the simulation experiment verification, the working state nodes of the proposed hybrid algorithm accounted for 1/5 of the total nodes, and the lifespan of the regional network could reach 1,098 seconds, with a duration better than common algorithms. The above results indicate that the algorithm can effectively improve energy utilization, expand its application in multiple fields, and provide new solutions for the efficient operation of wireless sensor networks.
To achieve optimal performance of municipal solid waste incineration (MSWI) process with nonstationary time-varying dynamics, a dynamic multi-objective operation optimization method (DSE-TrMOPSO), based on transfer le...
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To achieve optimal performance of municipal solid waste incineration (MSWI) process with nonstationary time-varying dynamics, a dynamic multi-objective operation optimization method (DSE-TrMOPSO), based on transfer learning, is proposed in this paper. First, the operation optimization models are established using data stream ensemble learning, where incremental updating and selective ensemble strategies are adopted to cope with changing working conditions. Second, a dynamic multi-objective particle swarm optimization algorithm based on transfer learning (CTrDMOPSO) is designed for optimization calculation. In this algorithm, the hierarchical clustering-based transfer learning strategy is proposed to construct the initial population with high quality. Afterwards, the knee point-based decision making is performed to determine the final setpoints of manipulated variables for index optimization. Then, the feasibility of the designed algorithm CTrDMOPSO is verified on the benchmark problems. Finally, the proposed DSE-TrMOPSO is applied to the MSWI process. The results demonstrate that the proposed method can achieve satisfactory operation performance in terms of combustion efficiency and nitrogen oxides emissions. Note to Practitioners-This study aims to develop an optimal operation method for the MSWI process with nonstationary time-varying dynamics. To achieve this goal, an operation optimization method based on transfer learning is proposed, which includes two key points, the operation optimization modeling and the dynamic multi-objective optimization. In practice, practitioners can implement the proposed method utilizing real-time data streams. The optimization objective models are constructed by data stream ensemble learning, and the time-varying dynamics can be captured with incremental updating and selective ensemble strategies. After that, the optimal setpoints of manipulated variables are derived by the designed dynamic multi-objective particle swarm optimizatio
This paper presents a novel time-varying formation (TVF) H-infinity tracking controller for multi-agent systems (MASs) under exogenous disturbances and time-varying delays. In order to address the issue that most of t...
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This paper presents a novel time-varying formation (TVF) H-infinity tracking controller for multi-agent systems (MASs) under exogenous disturbances and time-varying delays. In order to address the issue that most of the existing formation controllers are designed under the predetermined small time delay, a formation controller which can tolerate a large delay upper bound is designed. This is achieved by employing the Lyapunov functional method. At the same time, in order to suppress the influence of exogenous disturbances on the formation process, the robustness of the formation controller is improved through optimizing the H-infinity performance index. To derive the gain matrix of the formation protocol, a mere two linear matrix inequalities (LMIs) need to be resolved. On this basis, differential evolution (DE) algorithm is employed to further adjust and optimize the parameters of the formation controller. Finally, it is proved that the obtained results can be directly applied to resolving the leader-follower consensus problem. Two simulation experiments are carried out to verify the superiority and effectiveness of the proposed scheme, where four followers achieve the H-infinity formation tracking following the leader. Note to Practitioners-The motivation of this article is to address the TVF H-infinity tracking control problem for MAS under limited network communication resources. The TVF model is extensively applied in the flocking control domain, such as robot formation control, spacecrafts formation flight and so on. In the engineering application scenarios, the communication between agents usually has the following problems: 1) The information interaction among the agents is often influenced by exogenous disturbances and time delays;2) The existing formation tracking controllers are only suitable for handling predetermined small time delay. Thus, this paper presents a TVF H-infinity tracking controller with excellent robustness to deal with these problems, an
The rapid growth of 6G networks and the Internet of Things (IoT) has increased the need for advanced and adaptive data management. In response, our research brings together multiple devices, including smartphones, bic...
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The rapid growth of 6G networks and the Internet of Things (IoT) has increased the need for advanced and adaptive data management. In response, our research brings together multiple devices, including smartphones, bicycles, and in-vehicle systems, under a single user preference model to propose a novel proactive caching framework that surpasses traditional single-device approaches that give each device a unique set of preferences. Exploiting the advantages of both Device-to-Device (D2D) and Vehicle-to-Vehicle (V2V) communications, our proposed approach minimizes core network congestion and increases spectrum efficiency. At its core is the Cognitive File Caching Algorithm (CFCA), a proactive, scalable, and adaptive approach designed to address complex challenges involving varying file popularity in cities and user movement patterns. Our proposed CFCA improves caching efficiency by clustering users based on shared zones of interest and behavioral data, achieving a 27% improvement in network offloading and a 24% increase in cache hit ratio over existing benchmarks. Furthermore, the study explores the economic implications of proactive caching, establishing a strategic balance that ensures long-term profitability while minimizing high-cost caching inefficiencies. This study marks a paradigm advancement in proactive caching, setting the groundwork for future-ready 6G networks by combining user-centric data models, scalable algorithms, and adaptive file exchange mechanisms to address ever-increasing traffic needs.
During the operation of lithium-ion battery packs, there often exhibit certain abnormalities due to cell faults such as internal short circuit or unavoidable inconsistencies among cells, which affects the operation sa...
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This paper presents an innovative approach to anomaly detection in electric vehicle (EV) charging platforms, centered around four key innovations that significantly advance the field of charging infrastructure securit...
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This paper presents an innovative approach to anomaly detection in electric vehicle (EV) charging platforms, centered around four key innovations that significantly advance the field of charging infrastructure security. First, we introduce a statistically reliable data balancing methodology that addresses the critical challenge of anomaly sample scarcity, achieving a 47% improvement in detection accuracy compared to conventional sampling techniques. Second, we propose xDeepCIN, a novel deep learning architecture that uniquely combines Compressed Interaction Networks with low-order crossing structures, demonstrating a 28% reduction in feature sparsity compared to existing deep cross models. Third, we implement a hierarchical feature interaction mechanism that enables both explicit and implicit learning of high-order patterns, resulting in a 35% improvement in anomaly pattern recognition over traditional deep learning approaches. Fourth, we develop an adaptive processing framework for asynchronous heterogeneous data streams, reducing detection latency by 62% compared to current synchronous methods. Unlike prior studies that primarily focus on single-aspect improvements in either data processing or model architecture, our comprehensive approach simultaneously addresses data imbalance, feature sparsity, and processing efficiency. Experimental results across multiple real-world datasets demonstrate the framework's superior performance, achieving an AUC of 0.93 and F1-score of 0.97, representing substantial improvements over existing methods. This advancement has significant implications for the growing EV market, particularly in China, where our framework could prevent an estimated 85% of serious charging infrastructure failures.
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