Search Results (71)

To mitigate local congestion and address the adaptability limitations of traditional static routing under dynamic traffic, this paper proposes an end-to-end routing method based on a Graph Attention Network (GAT), termed Congestion-Aware Graph Attention Routing (CA-GAR). To alleviate the issue of local optima in traditional heuristic iterative optimization, we design a dynamic link cost optimization algorithm with multi-start parallel exploration. This algorithm employs a ”penalty–reselection–reward” closed-loop feedback mechanism, performing global searches from multiple random initial states to generate a high-quality, empirically near-optimal cost matrix as supervised labels. Building on this, CA-GAR leverages a multi-head attention mechanism to adaptively aggregate high-order topological features of nodes and edges, and incorporates a staged hierarchical hyperparameter optimization strategy to map real-time network states to link costs. Simulation results demonstrate that CA-GAR outperforms traditional static routing under light, medium, and heavy loads. Under high-load burst conditions, the method exhibits effective congestion avoidance capability, reducing end-to-end delay by approximately 50% and lowering the packet loss rate to as low as 2%. Compared with QLRA, CA-GAR shows promising performance in multi-path traffic splitting and possesses robust fast rerouting capabilities during node failures, thereby achieving intelligent traffic distribution and global load balancing. Full article

Parallel, data-intensive applications are now commonly executed on infrastructures that combine Cloud, Fog, and Edge resources. In these environments, execution takes place on devices with markedly different computational power and over networks whose latency and bandwidth can fluctuate over time. Under these conditions, overall performance is influenced not only by processing speed but also by communication delays arising from data dependencies between tasks. This leads to a basic issue: whether scheduling strategies developed under computation-focused assumptions continue to perform well once communication costs are made explicit. This work examines the behavior of simple and widely adopted scheduling heuristics when network effects are modeled directly within the system. No new scheduling algorithms are introduced. Instead, the analysis focuses on how execution time and monetary cost change for deterministic parallel workloads deployed on hierarchical Cloud–Edge infrastructures exposed to stochastic latency and bandwidth variations. For this purpose, we introduce CLOWNSim, a lightweight discrete-event simulation framework that supports large-scale Monte Carlo experiments on fixed task graphs, allowing infrastructural and scheduling effects to be examined independently of workload variability. The experimental analysis covers fully centralized Cloud deployments, intermediate Fog configurations, and resource-constrained IoT scenarios. Scheduling policies based on computational speed, execution cost, or random device selection are evaluated across these settings. In Cloud and Fog environments, communication latency and data transfers represent a substantial portion of the overall makespan, weakening the impact of scheduling decisions driven primarily by computation. In IoT scenarios, limited processing capacity becomes the main limiting factor, while communication overhead remains present but less influential in comparison. The results indicate that performance trends across the Cloud–Edge continuum cannot be attributed to scheduler choice alone. Execution behavior arises from the combined effects of workload structure, placement decisions, and network properties, with different elements becoming dominant depending on the deployment context. The proposed simulation framework offers a practical way to study these interactions and to assess cost–performance trade-offs under communication conditions that reflect realistic operating environments. Full article

With the evolution from Industry 4.0 to 5.0, flexible assembly scheduling must simultaneously address production efficiency, environmental sustainability, and human factors, while remaining adaptive to real-time disruptions. This study investigates the dynamic green scheduling problem in dual-resource Flexible Assembly Job Shops with worker learning and forgetting, aiming to minimize makespan and total energy consumption. To tackle this problem, a Hierarchical Dual-Agent Deep Reinforcement Learning algorithm (HAD-DRL) is proposed. The framework integrates a Heterogeneous Graph Neural Network to extract real-time workshop states and employs two collaborative agents, i.e., a high-level preference decision agent and a low-level scheduling execution agent. The upper agent dynamically adjusts the preference weights between economic and environmental objectives, while the lower agent generates corresponding scheduling actions. Unlike existing multi-agent methods that optimize a single objective at each step, HAD-DRL achieves adaptive coordination and balanced trade-offs among conflicting goals. Experimental results demonstrate that the proposed method outperforms heuristic and baseline DRL approaches in both objectives, validating its effectiveness and practical applicability for intelligent and sustainable manufacturing. Full article

To address the challenges of dense structural layouts, limited path feasibility, and stringent assembly constraints in cable routing within complex compartments of aerospace equipment, this paper proposes a cable path planning method that integrates Bidirectional Crossing Line Pruning (BCLP) with an improved ant colony optimization (IACO) algorithm. First, a hierarchical activation strategy for key obstacles is realized by constructing primary and extended crossing lines. On this basis, the BCLP algorithm is introduced, combining global perspective with local reduction capability to significantly reduce the complexity of the search space. Second, in line with cable assembly process requirements, a composite heuristic function is formulated by integrating obstacle-crossing cost and bending penalty. Additionally, a multi-objective-driven pheromone update model is developed to enhance the routing process’ feasibility and convergence performance. Experimental results across various aerospace cabling simulation scenarios demonstrate that the proposed method achieves an average reduction of 19.6% in multi-objective process cost and a 68.5% improvement in convergence efficiency compared to traditional visual graph methods combined with standard ACO. The approach provides effective support for the automation and intelligent planning of cable layouts in complex environments, offering strong potential for engineering applications. Full article

Automatic parking in constrained environments, such as dead-end roads and narrow parallel spaces, remains a challenge due to the low success rate and poor real-time performance of conventional planning algorithms. The paper proposes an entry-point guided path planning method that integrates heuristic search with hybrid A* and reeds-shepp curve to address the above limitations. By rapidly identifying the optimal initial parking pose, the proposed method ensures the kinematic feasibility and smoothness of the resulting trajectories. To further improve efficiency and safety in tight spaces, a hybrid collision detection mechanism is developed by combining a rectangular envelope with multi-circle fitting. The hierarchical geometric modeling approach significantly reduces computational cost while maintaining high detection accuracy. The method is validated through both simulations and real-vehicle experiments in vertical and parallel parking scenarios. Results demonstrate that in typical constrained scenarios, the average planning time is only 0.543 s, and the number of direction changes is maintained between 1 and 6, demonstrating superior computational efficiency and improved trajectory smoothness. These attributes make the algorithm highly suitable for practical deployment in advanced driver assistance systems and autonomous vehicles. Full article

Path planning aims to find a safe and efficient path from a starting point to an end point, and it has been well developed in fields such as robot navigation, autonomous driving, and intelligent decision systems. However, traditional path planning faces challenges in an uncertain rescuing environment due to limited sensing range and a lack of accurate obstacle information. In order to address this issue, this paper proposes an improved two-stage Theta* algorithm for handling multi-probability obstacle scenarios in unstructured rescue environments. First, a global probability raster map is constructed by integrating historical maps and expert prediction maps with probability weights quantifying the uncertainty in the spatial and temporal distribution of obstacles. Second, a probability-sensitive heuristic function (PSHF) is designed, and a Sigmoid function is used to map the probability field of obstacles, thereby enabling limited penetration in low-risk areas and enforced avoidance in high-risk areas. Furthermore, a multi-stage line-of-sight detection optimization mechanism is proposed, which combines probability soft threshold penetration and backtracking verification to improve the noise robustness. Finally, a hierarchical planning architecture is constructed to separate global probabilistic guidance from local strict obstacle avoidance, ensuring both the global optimality and local adaptability of the path. Extensive simulation results in mine rescue scenarios demonstrate that the proposed method achieves lower path cost and fewer path nodes compared to traditional A*, Dijkstra, and Theta* algorithms, while significantly reducing local replanning overhead and maintaining stable performance across multiple uncertain environments. Full article

Network slicing in 5G systems enables different Quality of Service (QoS) for heterogeneous Vehicle-to-Everything (V2X) applications, yet efficiently allocating resource blocks from both 5G base stations and roadside units (RSUs) across multiple slices remains challenging. Existing approaches either pre-assign users to slices or rely on population-based metaheuristic algorithms that cannot guarantee deterministic real-time performance within the stringent 20 ms latency requirements of vehicular networks. This study formulates the resource allocation problem as an integer programming model that jointly optimizes slice selection and resource allocation to maximize weighted system transmission rate while satisfying heterogeneous QoS constraints. We develop a constructive heuristic algorithm that employs a hierarchical allocation strategy prioritizing 5G resources before RSU resources, coupled with a backfilling mechanism to exploit the remaining resource block capacity. Numerical experiments across abundant 5G and limited resource scenarios demonstrate the algorithm’s effectiveness. First, comparing against Random baseline validates the optimization model’s value, achieving 21.4–24.9% higher weighted throughput in an abundant 5G scenario and 42.5–51.0% improvement under a limited resource scenario. Second, performance evaluation with 500 users shows the proposed constructive heuristic achieves optimal solutions in abundant 5G resource scenarios and 3.5–5.7% optimality gaps in limited resource scenarios, while maintaining an execution time of under 20 ms, which satisfies real-time requirements and executes faster than Gurobi, Simulated Annealing and Round-Robin. Third, scalability analyses across 400–700 users demonstrate favorable performance scaling, as the optimality gap decreases from 5.3% to 3.4% with execution times consistently below 20 ms. The proposed heuristic achieves the highest service admission count while maintaining near-optimal system weighted transmission rate performance, ranking second only to Gurobi solver. Compared with other baseline algorithms, the proposed heuristic delivers a superior balance between solution quality and computational efficiency, confirming its real-time feasibility for large-scale V2X network deployments. Full article

This study establishes a rigorous theoretical framework for Particle Swarm Optimization (PSO) convergence by introducing a novel symmetry assumption governing the algorithm’s stochastic components and a monotonicity condition between function values and Euclidean distance to the global optimum. Under this assumption, we prove linear convergence in expectation and almost sure linear convergence for a modified PSO algorithm with symmetric zero-mean random coefficients when parameters satisfy the explicit condition $w+{\displaystyle \frac{8(c_1^2+c_2^2){\sigma }_r^2}{1-w}}<1$. This provides the first closed-form relationship between inertia weight w, learning factors $c_1,c_2$, and random variance ${\sigma }_r^2$ that guarantees convergence. Building on this theoretical foundation, we develop three hierarchical applications: (1) static parameter design that replaces empirical tuning with theoretical calculation from desired convergence rates; (2) symmetric random factor optimization that eliminates directional bias and stabilizes velocity dynamics while preserving exploration variance; and (3) dynamic adaptive strategies that adjust parameters in real-time based on particle dispersion feedback. By bridging the gap between empirical performance and theoretical guarantees, this work transforms PSO from an empirically driven heuristic into a provably convergent optimization tool with rigorous performance guarantees for objective functions satisfying strict monotonicity between fitness and distance to the optimum (e.g., strictly convex functions). Full article

This paper proposes a coordinated planning method for distribution network lines considering geographical constraints and load distribution, aiming to improve the economy and engineering feasibility of distribution network planning. First, a hierarchical system of geographical constraints based on the Interval Analytic Hierarchy Process (IAHP) is established to systematically quantify the influence weights of spatial factors such as terrain undulation, ecological protection zones, and construction obstacles. Second, the density peak clustering algorithm and load complementarity coefficient are introduced to generate equivalent load nodes, and a spatially continuous load density grid model is constructed to accurately characterize the distribution and complementary characteristics of the load. Third, an improved A-star algorithm is adopted, which integrates a heuristic function guided by geographical weights and load density to dynamically avoid high-cost areas and approach high-load areas. Additionally, Bézier curves are used to optimize the path, reducing crossings and obstacle interference, thus enhancing the implementability of line layout. Verification via a real distribution network case study in a certain area of Guangdong Province shows that the proposed method outperforms traditional planning strategies. It significantly improves the economy, safety, and engineering feasibility of the path, providing effective decision support for distribution network line planning in complex environments. Full article

Energy optimization is crucial for unmanned aerial vehicle (UAV) path planning, particularly in complex wind-field environments. Most existing path-planning algorithms rely on simplified energy consumption models, which often fail to adequately capture the effects of wind fields. To address this limitation, a wind-adaptive lifelong planning A* algorithm (WA-LPA*) is proposed for energy-aware path planning in dynamic wind environments. WA-LPA* constructs a composite heuristic function incorporating wind-field alignment factors and integrates a hierarchical height-aware optimization strategy. Meanwhile, an adaptive replanning mechanism is designed based on the change characteristics of the wind field. Simulation experiments conducted across representative scenarios demonstrate that, compared to conventional algorithms that neglect wind-field effects, WA-LPA* achieves energy efficiency improvements of 5.9–29.4%. Full article

Autonomous exploration is essential for various mapping tasks, including data collection, environmental monitoring, and search and rescue operations. Unmanned aerial vehicles (UAVs), owing to their low cost and high maneuverability, have become key enablers of such applications, particularly in complex or hazardous environments. However, existing approaches often suffer from issues such as redundant exploration and unstable flight behavior. In this study, we propose a hierarchical exploration approach specifically designed for limited-field-of-view UAVs in geospatial mapping applications. The approach addresses these challenges through hybrid viewpoint generation, an innovative boundary exploration sequence, and a two-stage global path planning strategy. To balance exploration efficiency and computational cost, we adopt a hybrid approach that combines collision-free spherical sampling with adaptive viewpoint generation based on stochastic differential equations. This approach generates high-quality candidate viewpoints while minimizing computational overhead. Furthermore, we introduce a novel heuristic evaluation function to prioritize frontiers within small regions, thereby facilitating optimal path planning. Based on this formulation, the global coverage path is modeled as a traveling salesman problem (TSP). The two-stage global planning framework consists of an initial stage that applies a history-aware trajectory enhancement strategy with smoothing corrections, followed by a second stage employing a sliding-window TSP algorithm to construct the global path. This design mitigates motion inconsistencies caused by frequent heuristic updates and enhances flight stability and trajectory smoothness. To evaluate the performance of the proposed framework, we compare it with state-of-the-art approaches in both simulated and real-world environments. Experimental results demonstrate that our approach shortens flight paths and reduces exploration time, thereby improving overall exploration efficiency. Full article

Advances in low-power electronics and wireless communication have fueled the proliferation of interconnected metrological networks, increasing the need for traceable, networked measurement systems. This expansion, however, has created a surge in heterogeneous calibration tasks, while a scarcity of qualified experts and reference standards imposes severe resource constraints on remote calibration. Existing scheduling methods, though effective in homogeneous environments, typically lack integration of high-precision time-synchronization with heterogeneous resource coordination, limiting their use in time-critical metrology. To address this gap, we propose a multi-resource synchronized scheduling framework for remote calibration. We formulate the problem as a dual-container model that concurrently optimizes task mapping and temporal dependencies between edge instruments and cloud services. A two-stage heuristic algorithm is developed to efficiently map and schedule tasks in distributed client-server architectures by leveraging critical path analysis and hierarchical scheduling strategies. Simulations across diverse workloads and scales show our method outperforms existing baselines, achieving superior scheduling efficiency, scalability, and calibration accuracy. Full article

A Multi-Layer Perceptron (MLP), as the basic structure of neural networks, is an important component of various deep learning models such as CNNs, RNNs, and Transformers. Nevertheless, MLP training faces significant challenges, with a large number of saddle points and local minima in its non-convex optimization space, which can easily lead to gradient vanishing and premature convergence. Compared with traditional heuristic algorithms relying on a population-based parallel search, such as GA, GWO, DE, etc., the Besiege and Conquer Algorithm (BCA) employs a one-spot update strategy that provides a certain level of global optimization capability but exhibits clear limitations in search flexibility. Specifically, it lacks fast detection, fast adaptation, and fast convergence. First, the fixed sinusoidal amplitude limits the accuracy of fast detection in complex regions. Second, the combination of a random location and fixed perturbation range limits the fast adaptation of global convergence. Finally, the lack of a hierarchical adjustment under a single parameter (BCB) hinders the dynamic transition from exploration to exploitation, resulting in slow convergence. To address these limitations, this paper proposes a Flexible Besiege and Conquer Algorithm (FBCA), which improves search flexibility and convergence capability through three new mechanisms: (1) the sine-guided soft asymmetric Gaussian perturbation mechanism enhances local micro-exploration, thereby achieving a fast detection response near the global optimum; (2) the exponentially modulated spiral perturbation mechanism adopts an exponential spiral factor for fast adaptation of global convergence; and (3) the nonlinear cognitive coefficient-driven velocity update mechanism improves the convergence performance, realizing a more balanced exploration–exploitation process. In the IEEE CEC 2017 benchmark function test, FBCA ranked first in the comprehensive comparison with 12 state-of-the-art algorithms, with a win rate of 62% over BCA in 100-dimensional problems. It also achieved the best performance in six MLP optimization problems, showing excellent convergence accuracy and robustness, proving its excellent global optimization ability in complex nonlinear MLP optimization training. It demonstrates its application value and potential in optimizing neural networks and deep learning models. Full article

Climate change mitigation demands scalable, technologically mature solutions capable of addressing emissions from hard-to-abate sectors. Carbon Capture and Storage (CCS) offers one of the few ready pathways for deep decarbonization by capturing CO₂ at large point sources and securely storing it in deep geological formations. The long-term viability of CCS depends on well control strategies/injection schedules that maximize storage capacity, maintain containment integrity, ensure commercial deliverability and remain economically viable. However, current practice still relies heavily on manual, heuristic-based well scheduling, which struggles to optimize storage capacity while minimizing by-products such as CO₂ recycling within the high-dimensional space of interdependent technical, commercial, operational, economic and regulatory constraints. This study makes two contributions: (1) it systematically reviews, maps and characterizes these multidimensional constraints, framing them as an integrated decision space for CCS operations, and (2) it introduces an industry-ready optimization framework—Automated Optimization of Well control Strategies through Dynamic Time–Space Discretization—which couples reservoir simulation with constraint-embedded, hierarchical refinement in space and time. Using a modified genetic algorithm, injection schedules evolve from coarse to fine resolution, accelerating convergence while preserving robustness. Applied to a heterogeneous saline aquifer model, the method was tested under both engineering and financial objectives. Compared to an industry-standard manual schedule, optimal solutions increased net stored CO₂ by 14% and reduced recycling by 22%, raising retention efficiency to over 95%. Under financial objectives, the framework maintained these technical gains while increasing cumulative cash flow by 23%, achieved through leaner, smoother injection profiles that minimize costly by-products. The results confirm that the framework’s robustness, scalability and compatibility with commercial simulators make it a practical pathway to enhance CCS performance and accelerate deployment at scale. Full article

Aiming at the problems that traditional heuristic algorithms struggle to capture the complex correlations between damaged equipment and dynamically adjust maintenance task requirements in different task scenarios, the Graph Neural Network (GNN) and Deep Reinforcement Learning (DRL) optimization scheduling method for damaged equipment maintenance tasks is proposed, the purpose is to enhance the efficiency of optimization scheduling in dynamic scenarios. By constructing an attribute graph of damaged equipment and maintenance units, Graph Convolutional Network (GCN) and Graph Attention Network (GAT) are utilized to mine the correlations between nodes. A hierarchical reward function is designed in conjunction with DRL to dynamically adjust the multi-objective weights of maximizing importance, minimizing maintenance time. Hard and soft constraints such as maintenance skill matching, spare parts inventory, and threat thresholds are incorporated into the multi-objective optimization model to achieve real-time scheduling of maintenance tasks in an uncertain task environment. Case studies show that this method can effectively balance multi-objective conflicts through dynamic weight adjustment and online re-optimization mechanisms, making it suitable for multi-constraint task scenarios, compared with the Discrete Particle Swarm Optimization (DPSO) algorithm. GNN-DRL reduces the number of convergence iterations by 40%, improves the learning efficiency by 40%, and enhances the quality of the optimal solution by 11%, effectively improving the efficiency of maintenance task scheduling for damaged equipment. Full article