Search Results (720)

Cloud task scheduling is a critical component of cloud computing systems because it directly affects resource allocation, workload distribution, execution efficiency, and service cost. However, many metaheuristic algorithms suffer from population diversity loss, premature convergence, and an inadequate balance between global exploration and local exploitation when solving complex and large-scale optimization problems. To address these limitations, this study develops an Enhanced Human Urbanization Algorithm (EHUA) for numerical optimization and cloud task scheduling. Inspired by the collaborative resource-allocation behavior of modern logistics networks, three coordinated mechanisms are reformulated within the adventurer–city–citizen structure of the original Human Urbanization Algorithm: a logistics-hub-guided adaptive exploration mechanism, a supply–demand-based dynamic redistribution mechanism, and a cooperative logistics delivery exploitation mechanism. These mechanisms reduce excessive dependence on a single capital, adaptively regulate city search ranges, and strengthen citizen-level solution refinement. The performance of EHUA is evaluated on the CEC2014 and CEC2020 benchmark suites using convergence analysis, box plots, numerical statistics, Wilcoxon signed-rank tests, Friedman rankings, and ablation experiments. EHUA obtains the best mean fitness values on 20 of the 30 CEC2014 functions under both 30- and 50-dimensional settings, on 8 of the 10 CEC2020 functions at 10 dimensions, and on all 10 functions at 20 dimensions, demonstrating strong overall competitiveness and repeatability without implying universal superiority on every problem. EHUA is further applied to cloud task scheduling under workload scales ranging from 100 to 10,000 tasks. Considering comprehensive cost, monetary cost, execution time, and load cost, the proposed method consistently achieves low comprehensive scheduling costs and maintains favorable trade-offs among individual objectives as the workload increases. These results indicate that EHUA provides an effective and scalable optimization framework for complex benchmark problems and cloud task scheduling applications. Full article

This paper proposes a mismatch-index-driven coordinated flexible-step terminal-free distributed model predictive control (DMPC) scheme with an adaptive prediction horizon for asynchronous multi-agent systems (MASs) subject to bounded disturbances. The proposed approach explicitly exploits the inherent symmetry of the undirected communication topology among the agents, which ensures reciprocal information exchange, balanced cooperative interactions, and facilitates the rigorous analysis of consensus under asynchrony. By extending the generalized discrete-time control Lyapunov function (g-dclf) framework to the perturbed setting, we introduce a robust g-dclf together with a robust average decrease constraint that explicitly accounts for the worst-case effect of disturbances. A coordinated self-triggering mechanism, built upon the cost prediction mismatch index and the flexible-step execution strategy, is developed to simultaneously determine the inter-execution times and the number of control steps to be applied in each iteration. In addition, an adaptive shrinking prediction horizon strategy is incorporated to further reduce the computational complexity of the local optimization control problems (OCPs) as the agents approach consensus. The resulting robust flexible-step terminal-free DMPC (RFSTDMPC) algorithm is fully distributed, handles asynchronous communication, and operates without any stability-related terminal constraint. Recursive feasibility of each local OCP and input-to-state stability (ISS) of the overall closed-loop MAS are rigorously established under the symmetric network structure. Simulation results on the consensus problem of three perturbed nonholonomic vehicles demonstrate the effectiveness of the proposed scheme in achieving practical full-state stabilization while significantly alleviating the online computational burden. Full article

During the X-ray Diffraction (XRD) identification of the traditional Chinese medicine Os Draconis, the identification model often suffers from limited classification accuracy due to the difficulty in determining optimal parameters. To address this issue, this paper proposes a Hydrodynamic Improved Particle Swarm Optimization (HIIPSO) algorithm for the deep optimization of model parameters. In practical identification scenarios, the high complexity of XRD data poses severe challenges to the convergence speed and global search capability of optimization algorithms. To enhance model performance, this study introduces the interaction mechanism from fluid dynamics into the particle swarm optimization process. Specifically, HIIPSO incorporates a Voronoi neighbor topology to enhance population diversity and spatial distribution rationality. Concurrently, a hydrodynamic interaction mechanism is constructed to simulate the cooperative behavior of particles in a fluid environment, thereby effectively preventing the algorithm from falling into local optima. A theoretical analysis of the computational complexity of the HIIPSO algorithm in the parameter search task for XRD identification models was conducted, confirming that it falls within an ideal range for engineering applications. Statistical analysis of the experimental results demonstrates that, in the parameter optimization task for the Os Draconis identification model, the HIIPSO algorithm significantly outperforms traditional and other baseline algorithms across key metrics, including the optimal value, mean, standard deviation, and median of the objective function. The experimental data indicates that the HIIPSO algorithm can substantially improve the robustness and identification accuracy of the XRD-based Os Draconis identification model, making it an optimal solution for parameter optimization problems in the digital identification of complex mineral-based traditional Chinese medicines. Full article

Collaborative intelligent sensing in the Social Internet of Things (Social IoT) relies on distributed AI-enabled sensors to support complementary information sharing, multimodal perception, and real-time autonomous decision-making. Under high-load conditions, mismatches between resource provisioning and sensing quality of experience (QoE) can significantly degrade system performance in applications such as smart cities. To address this issue, this paper proposes a service priority-aware collaborative sensing support framework based on a joint next-generation passive optical network (NG-PON) and cooperative intelligent service-based radio access network (CIS-RAN) architecture. The framework enables edge AI-driven inference and distributed sensor collaboration in heterogeneous Social IoT environments. Service-slice-specific priority weights are assigned to optical network units (ONUs) and wavelengths according to the QoE requirements and latency sensitivity of sensing tasks, allowing dynamic wavelength tuning that prioritizes high-impact collaborative services. The utility of a centralized intelligent processing pool is formulated to achieve priority-consistent and efficient resource coordination under collaborative constraints. In addition, a multi-agent AI-driven optimization framework is employed to derive adaptive resource allocation strategies that incorporate service priorities while satisfying stringent service-level agreements (SLAs). Simulation results show that the proposed framework improves system-level proxy metrics, including total utility, wavelength satisfaction, and resource utilization, compared with representative baseline schemes. Full article

Digital innovation platform ecosystems can scale rapidly without becoming durable because broad participation does not ensure sustained value circulation. Existing models homogenize complementors, obscuring horizontal coopetition among distinct groups and its interaction with vertical governance. We examine how vertical and horizontal relations shape value-flow architecture and sustainability. Drawing on ecological symbiosis theory, we distinguish a platform orchestrator, technology-extending complementors, and service-integrating complementors. We construct an extended three-actor Lotka–Volterra model and use the Global Value-Flow Symmetry (GV) index and the continuous directional indicator ${\delta }_c$ to assess symmetry and direction. Designed for theory building rather than empirical calibration or testing, the analysis uses no empirical data and compares 35 vertical–horizontal configurations through deterministic simulation and sensitivity analysis. Results show the following: (1) Greater symmetry is associated with higher aggregate maintained output, but vertical backflow direction shapes its distribution; $\mathrm{G}\mathrm{V}$ must therefore be interpreted with ${\delta }_c$. (2) Under complementor-favoring vertical parasitism, horizontal mutualism expands complementor output but intensifies negative vertical backflow, simultaneously increasing aggregate output and reducing platform equilibrium—a cooperation paradox. (3) The same horizontal mutualism yields three focal outcomes across the examined vertical structures: Negative complementor-to-platform effects produce the cooperation paradox, absent effects produce value decoupling, and mutually positive vertical exchange produces systemic resonance. Thus, horizontal cooperation does not necessarily enhance sustainability; its effect depends on whether complementor gains feed back to support platform capability. This study advances platform-ecosystem sustainability research and offers theoretical guidance for optimizing value backflow and cooperative governance. Full article

To address the issue of unbalanced path allocation in multi-UUV cooperative operations under inhomogeneous ocean environments during full-coverage search missions, this paper proposes a heterogeneous UUV path planning method that integrates a hybrid waypoint deployment strategy with a dual-chromosome co-evolutionary genetic algorithm. First, heterogeneous UUVs are adaptively assigned to sub-regions according to the search value of the sea area, and a combination of Poisson sampling and Voronoi iterative refinement is adopted to complete the layout of detection points. Subsequently, connectivity-constrained K-means clustering is introduced to decompose the multi-traveling salesman problem (MTSP) into several independent TSP sub-problems. Finally, a dual-chromosome encoding scheme for task sequences and split points is designed, and a penalty matrix is incorporated into the fitness function to account for obstacle avoidance constraints, thereby establishing an integrated genetic-algorithm-based solution framework that incorporates both decomposition and obstacle avoidance. Simulation results demonstrate that the proposed method reduces the number of planned detection points by 12.4%, 12.8%, and 9.3% compared with baseline methods in circular, rectangular, and irregular sea areas, respectively, while the optimal path lengths are shortened by 5.8%, 4.5%, and 5.9%. Moreover, the cooperative mission time with four UUVs is reduced by 73.9%, 72.7%, and 71.2% relative to a single UUV, demonstrating an approximately linear speedup relative to the number of UUVs. Convergence analysis and extended experiments on 15 instances further confirm the algorithm’s solution stability and robustness under varying regional scales, shapes, and obstacle configurations. These results validate that the proposed approach not only reduces the number of deployment points and path cost, but also effectively balances obstacle avoidance and multi-robot load distribution. Full article

To alleviate renewable energy curtailment and the high operating costs arising from the temporal and spatial mismatch of distributed generation, this paper develops a cross-park dispatch optimization approach for power systems under the joint participation of carbon trading and green certificate trading (GCT). The proposed approach aims to improve system flexibility and economic performance in coordinated multi-park operation. Specifically, adjustable resources in different parks are dispatched in a coordinated manner, and the total comprehensive operating cost is taken as the optimization objective. In addition, the Alternating Direction Method of Multipliers (ADMM) is adopted to determine inter-park electricity trading prices and exchanged power in a distributed framework. Furthermore, an asymmetric bargaining model is introduced to distribute the cooperative benefits, ensuring a balance between fairness and incentive compatibility. Simulation results demonstrate that inter-park electricity interaction reduces generation costs by 5.29%. The integration of carbon and green certificate trading further reduces costs by 7.4%. After asymmetric bargaining-based benefit allocation, the operating costs of parks with higher contributions decrease by up to 10.34%. The results conclude that the proposed strategy effectively leverages the complementary advantages of multi-park resources and optimizes the synergy between carbon markets, green certificate markets, and physical dispatch. Full article

Hydrogen-bond organization plays a central role in determining the structure and properties of water from molecular to macroscopic scales. In this study, we propose a geometry-based descriptor for small hydrogen-bonded water clusters, (H₂O)_n, with n = 2–6. The central element of the proposed geometric framework is the dimensionless geometric index, S_n = d/l, where d is the center-to-molecule distance in a cluster configuration and l is the nearest-neighbor O···O distance associated with hydrogen-bonded water molecules. The geometric descriptor is not intended to replace quantum-chemical calculations or to provide a direct measurement of hydrogen-bond energy, lifetime, or number. Instead, it provides a compact geometric framework for describing the structural organization of small hydrogen-bonded water clusters. The obtained geometric trend is compared with selected Nuclear Magnetic Resonance (NMR), Møller–Plesset perturbation theory (MP2), and radial distribution function data as complementary qualitative and semi-quantitative references. The proposed geometric index S_n = ${\displaystyle \frac{d}{l}}$ was further compared with MP2 quantum-chemical O···O distances for (H₂O)_n clusters, n = 2–6, using the oxygen atoms as structural nodes of the hydrogen-bonded motifs. This comparison showed that the exponential increase in S_n is consistent with the characteristic O···O donor–acceptor length scale of approximately 2.8 Å, linking the geometric framework with calculated molecular geometries. Over the limited interval n = 2–6, the geometric index Sn increases monotonically and nonlinearly with cluster size. The quantum-chemical reference data previously reported in our study, comprising GIAO-DFT-calculated ¹H chemical shifts obtained for MP2-optimized water-cluster geometries, show a rapid nonlinear increase from the dimer to the pentamer, followed by the onset of saturation in the pentamer–hexamer range. The semi-empirical stabilization parameter evaluated in the present study indicates increasing relative stabilization, with a reduced incremental change around n ≈ 4–5. The qualitative consistency of these size-dependent trends supports the use of Sn as a compact geometric descriptor of hydrogen-bond organization in small water clusters, without interpreting it as a direct quantitative measure or mechanistic framework of hydrogen-bond cooperativity. Importantly, liquid water is not treated as a system of closed cyclic clusters; cyclic motifs are used only as frameworked geometric reference configurations for small hydrogen-bonded aggregates. The geometric trend is qualitatively compared with selected quantum-chemical, spectroscopic, and radial distribution function data and should be regarded as an empirical geometric approximation over the limited interval n = 2–6. These findings indicate that geometric, spectroscopic, and quantum-chemical descriptors reflect related, but not identical, aspects of hydrogen-bond organization. The proposed approach links cluster geometry, O···O intermolecular distances, and hydrogen-bond connectivity in a simplified geometric description. Full article

Open wireless propagation makes unmanned aerial vehicle (UAV) links vulnerable to eavesdroppers distributed over roads, building clusters, or other continuous regions. This paper proposes a low-rank threat-subspace method for cooperative secure beamforming from distributed ground transmitters to a legitimate UAV. Steering vectors sampled over one or multiple azimuth–elevation threat regions are concatenated into a training matrix, whose dominant left singular vectors compactly represent regional exposure. The legitimate steering vector is projected onto the orthogonal complement of this subspace and power-normalized. We prove global optimality of the normalized projection for every feasible retained rank, derive a leakage bound from the first discarded singular value, and introduce uncertainty padding for independently mismatched region boundaries. Simulations evaluate disconnected and volumetric regions, deterministic geometries, Rician scattering, channel and phase errors, non-colluding and colluding eavesdroppers, and covariance-reconstruction and sampled peak-leakage baselines. In the default setting, six modes retain 98% of the sector energy, and the proposed method reduces average leakage to −26.25 dB, compared with −19.41 dB for pointwise nulling and −9.89 dB for maximum-ratio transmission. Independent boundary-error tests show that interval padding stabilizes leakage at the cost of desired gain, while multi-region tests quantify the progressive increase in effective rank. The results establish both the applicability limits and the low-overhead advantages of spatial-structure-based secure beamforming. Full article

The rapid growth of AI-driven workloads, IoT ecosystems, and distributed digital services has exposed fundamental limitations in existing cloud and edge infrastructures, particularly in guaranteeing robust data privacy under emerging quantum threats. Current blockchain-based systems provide integrity and decentralization but rely predominantly on computational cryptography and access-control mechanisms, leaving them vulnerable to future quantum adversaries and large-scale inference attacks. In this paper, we introduce Data Communities as a novel paradigm for privacy-preserving, blockchain-enabled cooperative digital infrastructures, formalized within the Cooperative Digital Infrastructure (CDI) framework. Our approach integrates three complementary privacy protection layers: (i) MuReQua, a quantum-resilient blockchain consensus mechanism leveraging CQKD for cryptographic robustness against Shor-type attacks; (ii) DeSSE, an information-theoretically secure distributed storage model based on n × m fragmentation, ensuring zero information leakage below reconstruction thresholds; and (iii) a multi-tier data sovereignty model (C0–C3) enforcing policy-driven data locality and regulatory compliance across federated nodes. We formalize privacy guarantees through an adversarial model encompassing classical, quantum, insider, and governance-level threats, and demonstrate that the proposed architecture achieves information-theoretic confidentiality, forward secrecy, and attack-resilient distributed governance. A privacy leakage analysis shows that the probability of data reconstruction under sub-threshold compromise is identical to zero, outperforming conventional blockchain storage models based on encryption alone. Simulation and case study results indicate that Data Communities achieve up to 99.999% service availability, 55% reduction in external data exposure, and 22–35% carbon-aware optimization, while maintaining strict privacy guarantees across distributed environments. Compared with existing blockchain systems (e.g., Ethereum, Hyperledger Fabric), the proposed framework shifts privacy protection from access-control and pseudonymity to structural, information-theoretic privacy by design. Overall, the results establish Data Communities as a scalable and quantum-resilient foundation for next-generation privacy-preserving blockchain infrastructures, bridging distributed AI, secure storage, and cooperative governance under a unified formal model. Full article

In the context of economic globalization, effective cooperation and information sharing are critical for enhancing the efficiency and competitiveness of supply chains. Fresh agricultural product supply chains face distinctive challenges arising from high perishability, rapid demand fluctuations, and information asymmetry among supply chain members. This study develops an analytical framework comprising the following three conceptually connected but independently calibrated modules: an EOQ cost module, a comparative profit module, and a two-period, two-task dynamic principal–agent module. The modules are connected through the common economic logic of value creation, value distribution, and incentive design, rather than through a one-to-one numerical mapping. Using operational and financial information from the Erli River Crab supply chain in Panshan County, the EOQ and comparative profit models are independently calibrated at different decision scales to evaluate the benchmark economic effects of information-sharing-enabled coordination. The EOQ analysis uses annual aggregate operational quantities, whereas the comparative profit analysis uses a normalized transaction-demand scale; therefore, their numerical quantity magnitudes are not intended for direct one-to-one comparison. Within the EOQ module, information-sharing-enabled coordination reduces total relevant supply chain cost by 8.41% relative to the farmer-led decentralized benchmark and by 60.75% relative to the retailer-led decentralized benchmark. The difference arises because the farmer-led batch quantity is closer to the coordinated optimum, whereas the retailer-led order quantity is substantially smaller, implying a higher modeled frequency of upstream setup activities and a larger setup-cost component under the benchmark parameterization. These results capture the joint effect of full information availability and coordinated batch optimization under two alternative decentralized decision regimes. Within the comparative profit module, over the benchmark wholesale-price interval, the coordinated full-information-sharing benchmark increases total supply chain profit by 1.38–2.30% relative to the decentralized no-information-sharing benchmark. Under the unchanged-wholesale-price comparison, the farmer’s profit increases by 13.21–17.65%, whereas the retailer’s profit decreases by 1.74–3.11%. These results identify positive aggregate value creation and an asymmetric initial allocation under the linear-demand and unchanged-wholesale-price benchmark. This asymmetric allocation is benchmark-specific rather than an unavoidable consequence of information-sharing-enabled coordination, because a negotiated transfer or wholesale-price adjustment can redistribute the additional surplus. The extended analysis derives a participation-compatible transfer interval within which both the farmer and the retailer are weakly better off than under decentralized decision-making. Within the dynamic principal–agent module, a case-motivated illustrative simulation using standardized benchmark parameters shows that more informative intertemporal performance signals strengthen the optimal second-period incentive coefficient, whereas greater risk exposure limits the appropriate intensity of performance-based compensation. Continuation–payoff and ratchet-like effects jointly shape first-period information-sharing effort. Together, the three modules show how information-sharing-enabled coordination affects operational efficiency, surplus allocation, and intertemporal incentives. Full article

Existing quadruped robots typically place their center of mass (CoM) near the geometric center of the body to achieve structural symmetry and simplify control design. In contrast, many quadrupedal animals capable of agile running exhibit a pronounced anteriorly biased mass distribution, with the CoM located closer to the front of the body. This biological characteristic motivates a re-examination of whether a geometrically centered CoM necessarily corresponds to dynamically balanced loading between the fore- and hindlimbs during high-speed locomotion. To address this question, this study investigates the influence of longitudinal CoM position on load distribution during high-speed straight-line locomotion of quadruped robots. A unified analytical framework is established by combining whole-body force and pitch moment equilibrium, sagittal-plane kinematics, and Jacobian-based force-to-torque mapping, thereby linking longitudinal CoM position, foot-end support forces, and joint loads. Simulation validation is conducted on the Black Panther 2 quadruped robot using four central-body CoM configurations, denoted as ×0, ×5, ×10, and ×15. In the primary evaluation at 5 m/s, shifting the CoM forward from ×0 to ×15 reduces the absolute median fore–hindlimb differences in support force and joint torque by approximately 86.6% and 93.4%, respectively, indicating a transition from hindlimb-dominated loading toward cooperative load sharing between the fore and hindlimbs. Independent training runs with multiple random seeds further confirm the robustness of this load-redistribution trend to reinforcement learning variability. Consistent behavior is also observed at 8 m/s, while no evident degradation in turning response or locomotion stability is found under the tested turning and randomly generated rough-terrain conditions. These results demonstrate that a moderate forward shift of the longitudinal CoM can alleviate hindlimb load concentration and promote a more balanced fore–hindlimb load distribution, providing a theoretical basis for the morphological design and control optimization of high-speed quadruped robots. Full article

Distributed integrated energy microgrids support the low-carbon transition of regional energy systems. However, multi-agent trading among microgrids still faces insufficient cross-market coordination, weak low-carbon incentives, and difficulties in fair benefit allocation. To address these issues, this paper proposes a cooperative-game-based low-carbon optimal operation strategy for multi-microgrids using multi-agent deep reinforcement learning. First, an energy-carbon-green certificate peer-to-peer coordinated trading mechanism and a green-carbon offsetting-based dual-incentive model are developed to link energy exchange, carbon quota adjustment, and green certificate circulation. Second, a Nash bargaining-based cooperative game model is formulated for multi-commodity P2P trading to maximize coalition benefits and ensure a fair allocation of surplus. Finally, the cooperative game is transformed into a Markov decision process, and a centralized training and decentralized execution framework with homogeneous agents is constructed based on the multi-agent soft actor-critic algorithm. Case studies using data from the Yangtze River Delta region of China show that the proposed method achieves a 1.25% optimality gap compared with the centralized MILP benchmark and reduces the coalition operating cost by 8.19% relative to independent operation. The carbon trading costs of the three microgrids are reduced by 37.27%, 40.13%, and 33.82%, respectively, verifying the economic applicability of the proposed method. Full article

To address the dynamic communication topology switching, asynchronous perception information, and uncertainty caused by vehicle mobility in the cooperative control of a vehicle-charger pile-grid under industrial Internet environments, this paper proposes a cooperative optimal control method based on decentralized holistic sensing graph-based estimation. First of all, this method constructs a time-varying weighted directed graph by using decentralized holistic sensing data obtained from the industrial Internet to characterize the dynamic evolution of communication topologies in real time. Secondly, a distributed graph estimator relying solely on local perception information is designed, enabling each agent to predict online its neighbor set and link reliability over a short future horizon based on its own position, the motion trends of nearby objects, and historical link states. On this basis, the graph-based estimation results are embedded as a feedforward compensation term into the consensus control law, forming a predictive graph consensus control algorithm that enables the system to proactively adjust control inputs before topology switching occurs, achieving a paradigm shift from “passive response” to “active pre-compensation.” Meanwhile, an Age of Information (AoI)-aware event-triggered mechanism is introduced, where broadcasting is triggered when the state error exceeds a threshold or the AoI approaches its upper bound, significantly reducing communication load while ensuring control accuracy. Finally, simulations are conducted on a modified IEEE 33-bus distribution system comprising 61 agents (20 electric vehicles, eight charging stations, and 33 grid nodes). The results show that, compared to the event-triggered consensus method without prediction, the proposed method reduces the steady-state error by 40.1%, shortens the convergence time by 40.5%, and decreases the number of broadcasts by 36.5%. In a large-scale system with 169 agents, the proposed method still maintains the highest accuracy, the fastest convergence speed, and the lowest communication overhead, while meeting real-time computational requirements. This method can fully exploit the spatiotemporal redundancy of decentralized holistic sensing, offering a new solution for efficient, robust, and low-cost cooperative control of “vehicle–charger–grid” under industrial Internet environments. Full article

The integration of large-scale distributed energy resources (DERs) into distribution networks (DNs) brings challenges to the effective control of DNs. In traditional approaches, mathematical or reinforcement learning (RL)-based solution algorithms are commonly used. However, the exponential increase in the number of DERs reduces the effectiveness of these strategies. Mathematical methods struggle to cope with the dynamic uncertainty caused by the high penetration of renewable energy, while RL algorithms relying on global data training may violate multi-agent privacy protocols. This paper proposes a DNs cooperative optimization method based on resource aggregation and RL. To reduce optimization dimensionality and ensure the privacy of resource data, a dynamic aggregation strategy is employed to aggregate a large number of distributed energy resources into aggregated entities, and the adjustable active–reactive power boundaries of each aggregated entity are derived. To fully exploit the regulation capability of DNs, data centers (DCs), as novel devices, are considered as flexible loads. To improve the convergence speed of model training and decision-making accuracy, evolution strategies (ES) and prioritized experience replay (PER) are integrated into the Soft Actor-Critic (SAC) algorithm, respectively. The proposed method is validated on the IEEE 33-bus and IEEE 123-bus systems. The results demonstrate the effectiveness and superiority of the proposed method in ensuring the secure operation of DNs. Full article