Mission-Oriented Combat Effectiveness Evaluation Model For Intelligent Equipment System-Of-Systems,
2026
System Engineering Research Institute, Academy of Military Science, Beijing 100101, China
Mission-Oriented Combat Effectiveness Evaluation Model For Intelligent Equipment System-Of-Systems, Long Zhang, Wenbo Huang, Xuanming Feng, Zhen Lei, Ying Wang
Journal of System Simulation
Abstract: A hierarchical evolutionary adaptive learning (HEAL) framework is proposed to address the problems in the operational simulation of intelligent equipment systems under complex adversarial environments, including fragmented hierarchical decision-making, unclear mapping between algorithmic indicators and operational effectiveness, and the lack of adaptive evolution capability of simulation models. A multi-layer learning architecture that integrates robust autonomous decision-making for single units, emergent collaboration of system networks, and game-theoretic evolution of system-of-systems strategies is constructed by following the “micro-meso-macro” system-oriented hierarchical modeling paradigm. A cross-layer closed-loop coupling among learned policies, cooperative structures, and operational effectiveness is achieved by combining bottom-up behavioral abstraction …
Multimodal Perception-Based Trajectory Planning Method For Uavs In Forest Environments,
2026
State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining, Anhui University of Science & Technology, Huainan 232001, China; School of Artificial Intelligence, Anhui University of Science & Technology, Hefei 231131, China
Multimodal Perception-Based Trajectory Planning Method For Uavs In Forest Environments, Dequan Li, Jiahao Jiang
Journal of System Simulation
Abstract: To address the requirements for low-latency local planning and safe obstacle avoidance in UAV forest-fire patrol under dense tree-trunk occlusion and locally invalid depth observations, this paper proposes a learning-based local trajectory planning method that fuses depth images, local occupancy distance maps, candidate-level collision-risk assessment, and pre-execution safety reranking. The method uses depth images, local occupancy distance maps, motion states, and target direction as inputs and generates 15 quintic-polynomial candidate trajectories in parallel using 3 × 5 angular motion primitives. During training, candidate safety costs, ranking supervision, and near-collision risk labels are constructed. During inference, online LiDAR point clouds …
Manifold Alignment And Dual Prediction-Based Adaptive Dynamic Multi-Objective Evolutionary Algorithm,
2026
School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China
Manifold Alignment And Dual Prediction-Based Adaptive Dynamic Multi-Objective Evolutionary Algorithm, Xuezhi Yue, Chao Xiong, Yanshen Jiang, Zhaolu Guo, Hu Peng
Journal of System Simulation
Abstract: To address prediction failure, manifold topology distortion, and insufficient adaptability in environmental response and operator configuration in existing prediction-based algorithms for dynamic multi-objective optimization under complex nonlinear environments and small-sample settings, this paper proposes a manifold alignment and dual prediction-based adaptive dynamic multi-objective evolutionary algorithm, termed MADP-DMOEA. When the environment changes, the algorithm decomposes the decision-variable manifold into center and shape components, effectively aligns manifolds at adjacent time instants via bipartite optimal matching, and identifies environmental change severity using geometric displacement. A dual prediction model is developed, in which a GRNN performs center extrapolation under severe changes, while an …
Cooperative Hunting Method For Multiple Usvs Based On Improved Maddpg,
2026
Naval Submarine Academy, Qingdao 266000, China
Cooperative Hunting Method For Multiple Usvs Based On Improved Maddpg, Yonghao Zhang, Changyou Xue, Xueman Fan, Zhuo Cheng
Journal of System Simulation
Abstract: A cooperative hunting method based on improved multi-agent deep deterministic policy gradient (MADDPG) is proposed to address the challenges of non-stationary environments, sparse rewards, and partial observations in the cooperative hunting of targets with unknown motion patterns by multiple unmanned surface vessels (USVs). A Markov game model for hunting is built, and a multidimensional guided reward shaping function incorporating collision avoidance, hunting success, and energy consumption penalties is designed to mitigate the sparse reward problem. ResNet residual networks are introduced to construct the Actor and Critic networks and thus enhance the representation capability of deep networks for complex state-action …
Five-Axis Multi-Station Process Planning Based On Multi-Task Graph Reinforcement Learning,
2026
Faculty of Mechanical Engineering & Mechanics, Ningbo University, Ningbo 315211, China
Five-Axis Multi-Station Process Planning Based On Multi-Task Graph Reinforcement Learning, Junjie Liu, Jianlei Xu, Litao Ju, Yanhai Hu, Qinghao Zhang, Qiang Liu
Journal of System Simulation
Abstract: A hierarchical intelligent planning architecture integrating multi-task graph and masked proximal policy optimization (MTG-MPPO) is proposed to address the challenges of factorial expansion of the state space and multi-objective interference in five-axis multi-feature machining. An attributed adjacency graph integrating boundary representation and machining direction vectors is constructed. Probabilistic soft labels are output through a multi-task network to quantify process flexibility and thus achieve prior topological pruning of solution space. An MDP model integrating directed acyclic graph masks and dense reward shaping is built. A Monte Carlo (MC) optimization mechanism based on policy distribution is introduced to coordinate cutting, tool-changing, …
Dynamic Multi-Objective Optimization Algorithm Based On Residual-Driven Two-Stage Prediction And Its Application,
2026
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214000, China
Dynamic Multi-Objective Optimization Algorithm Based On Residual-Driven Two-Stage Prediction And Its Application, Xin Zhang, Han Wang, Pengjiang Qian, Wei Fang
Journal of System Simulation
Abstract: A dynamic multi-objective optimization algorithm based on residual-driven two-stage prediction (RDTP) is proposed to address the dilemma of prediction strategies in dynamic multi-objective optimization struggling with insufficient samples in the early stage and low accuracy in the later stage. In the initial stage, if an environmental change is detected, grey prediction, which is fast and suitable for small samples, is employed to generate individuals for the new environment. A residual analysis is performed to determine whether to switch to a LSSVM to improve prediction accuracy as more samples become available. The Pareto front is clustered to select detection objects. …
Optimization Of Mobile Power Resource Allocation And Scheduling For Multi-Point Emergency Response,
2026
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Optimization Of Mobile Power Resource Allocation And Scheduling For Multi-Point Emergency Response, Ding Ma, Tao Zhang, Bo Jiang, Yajie Liu, Shuijia Li
Journal of System Simulation
Abstract: In extreme event scenarios, urban mobile power resources (MPRs) suffer from uneven spatial distribution and capacity mismatch during emergency scheduling. To achieve globally rational allocation and improve resource configuration and power supply guarantee, a two-stage decision-making method for MPR allocation and scheduling is proposed for multi-point emergency power demands. First, the characteristics of MPRs and travel energy consumption are modeled. In the first stage, an allocation model considering demand satisfaction, capacity matching, and dispatch time is established to solve the macro-level matching problem between MPRs and demand points. In the second stage, an interface scheduling model is constructed based …
Coordinated Optimization Of Heterogeneous Fleet Configuration And Multimodal Routing For Industrial Clusters,
2026
Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650500, China
Coordinated Optimization Of Heterogeneous Fleet Configuration And Multimodal Routing For Industrial Clusters, Renxin Xiao, Yifan Xu, Guisheng Chen, Nan Pan
Journal of System Simulation
Abstract: A two-level optimization model integrating heterogeneous electric vehicle fleet configuration with multimodal path coordination is built to address the challenges in industrial cluster freight transportation involving electric heterogeneous fleet scheduling, such as diverse tasks and stringent constraints on energy consumption and delivery timeliness. Considering the performance coupling characteristics of heterogeneous electric fleets in terms of load-energy consumption-endurance, based on the coordination of multimodal paths and fleet configuration coding, the upper level employs the GWO to optimize task splitting and fleet configuration. The lower level adopts a multi-neighborhood SA algorithm to achieve path coordination scheduling. Simulation results show that the …
Simulation Study On Circular-Track Rgv Scheduling Based On Railway Operating Rules,
2026
School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
Simulation Study On Circular-Track Rgv Scheduling Based On Railway Operating Rules, Hualan Wang, Li Wang, Zhongning Fu
Journal of System Simulation
Abstract: To address the limitations of circular RGV systems under high-density operating conditions, including a simple operational structure and limited parallel operation capability, this study investigates track structures and dispatching mechanisms for complex operational requirements. Based on the agent-based modeling method implemented on the AnyLogic platform, a simulation model of an RGV system with a nested circular track layout was developed, in which an embedded sub-loop equipped with track-switching devices is introduced into the traditional circular track structure. Operating and dispatching rules suitable for the nested circular structure are designed by incorporating the concepts of section operation and overtaking in …
Air Defense Resource Allocation And Reconstruction Method Based On Kggom,
2026
Beijing Institute of Electronic System Engineering, Beijing 100854, China
Air Defense Resource Allocation And Reconstruction Method Based On Kggom, Jiaoxiong Chen, Chenglong Zhang, Yuqiang Zhao, Xianglin Li, Qiang Zhao
Journal of System Simulation
Abstract: To address the challenge that traditional solving methods cannot meet real-time requirements in the intelligent allocation and reconstruction of air defense operation resources, this paper focuses on an intelligent solving method for the dynamic allocation and reconstruction of intercept timing sensor-weapon-target. A multi-constraint model is established with damage effectiveness and operation cost as optimization objectives, and a knowledge-guided genetic optimization method (KGGOM) is proposed. Through knowledge-guided population generation and evolution, constraint-aware operators with correction mechanisms, and multi-path search combined with simulated annealing for gene correction, it improves its optimization capability and speed. Historical superior solution retention and dynamic elite …
Hybrid Jaya-Transformer-Based Task Scheduling Algorithm For Super-Agile Satellites,
2026
School of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China
Hybrid Jaya-Transformer-Based Task Scheduling Algorithm For Super-Agile Satellites, Haoyu Zhao, Guanghui Zhang, Lanting Hao, Meijia Guo
Journal of System Simulation
Abstract: Super-agile satellite task scheduling is characterized by complex constraints and tight coupling among task transitions. To address the relatively long online solution times of traditional heuristic algorithms, this paper establishes a super-agile satellite task scheduling model that aims to maximize the task profit ratio while considering constraints on visible time windows, attitude transition time, energy, and storage capacity. A Transformer is used to learn task requirements, visible time windows, and global correlations among tasks, and a dynamic mask and a task scheduler are incorporated to generate feasible task sequences. To enhance the model's ability to learn complex task correlations …
Aerial Refueling Scheduling Plan Generation Method For Multi-Position Task Management *,
2026
Air Force Engineering University, Xi'an 710038, China
Aerial Refueling Scheduling Plan Generation Method For Multi-Position Task Management *, Jiachen Hu, Huanyu Li
Journal of System Simulation
Abstract: An aerial refueling scheduling plan generation method based on hierarchical hybrid strategy is proposed to address the high complexity of aerial refueling scheduling for multi-position tasks and the difficulty for traditional manual operations or single-position-oriented solutions to manage tasks effectively. A multi-level strategic framework based on fixed-rule heuristics, rolling horizon optimization, and steady-state cyclic scheduling is developed and integrated into a hierarchical hybrid strategy to balance scheduling efficiency, optimization capability, and emergency response capability. Simulation results demonstrate that the proposed method effectively generates feasible refueling scheduling plans, producing viable plans under both undisturbed rotational operations and disturbed dynamic support …
Flight Test Mission Planning Method Based On Adaptive Large Neighborhood Search,
2026
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Flight Test Mission Planning Method Based On Adaptive Large Neighborhood Search, Luofu Wu, Xinyang Wu, Lunhao Ju, Guopeng Song, Rui Wang, Jiang Jiang
Journal of System Simulation
Abstract: Flight test mission planning is critical to aircraft performance evaluation, but traditional manual methods often struggle with extensive solution spaces and complex constraints. This paper constructs an overall flight test mission planning model and introduces a flight test planning algorithm (FTPA) based on adaptive large neighborhood search. FTPA utilizes Kahn topological sorting to generate an initial test-item priority sequence, which is then decoded into an initial solution, and jointly schedules flight test items and test aircraft within the adaptive large neighborhood search framework. A comparison with the CPLEX solver shows that the FTPA algorithm yields solutions with a gap …
Large Language Model-Assisted Control Framework For Mixed Remote Sensing Satellite Constellations,
2026
School of Automation, Central South University, Changsha 410083, China
Large Language Model-Assisted Control Framework For Mixed Remote Sensing Satellite Constellations, Guohua Wu, Ziyan You, Lei Li, Yi Gu, Shuanglin Li
Journal of System Simulation
Abstract: A LLM-assisted control framework for mixed remote sensing satellite constellations is proposed to address the problem that the control and scheduling of mixed remote sensing satellite constellations cannot process users' raw natural language observation requirements and realize full-process automation from requirement input to scheduling schemes. LLMs are utilized to enhance the construction of domain knowledge graphs, requirement understanding and task characterization, and realize intelligent generation of scheduling schemes and optimal solution of scheduling models. A scheduling scheme generation method based on the "planning and execution" dual-Agent architecture is designed. Combined with the constellation scheduling toolset, the capability boundary of …
On-Orbit Refueling Mission Planning Of Satellites Based On Improved Nsga-Ii,
2026
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China; State Key Laboratory of Space System Operation and Control, Xi'an 710043, China
On-Orbit Refueling Mission Planning Of Satellites Based On Improved Nsga-Ii, Guozheng Li, Rui Wang, Yi Lu
Journal of System Simulation
Abstract: An improved NSGA-II solution framework based on multi-tree encoding is proposed to address the problems of high modeling complexity under large-scale scenarios and the tendency of traditional algorithms to fall into local optima. Based on multi-tree modeling, the complex mixed-integer programming problem is transformed into a multi-tree configuration optimization problem, thus reducing the constraint handling difficulty and improving the search efficiency of the solution space. Multi-tree crossover and mutation operators as well as the calculation method for objective functions are designed. A large neighborhood search (LNS) strategy is introduced, and the global exploration and local exploitation capabilities of the …
High-Fidelity Dynamic Digital Twin Modeling Method For Permanent Magnet Synchronous Motor,
2026
School of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
High-Fidelity Dynamic Digital Twin Modeling Method For Permanent Magnet Synchronous Motor, He Lai, Jinquan Yang, Cong Peng, Bei Li
Journal of System Simulation
Abstract: Conventional mechanism models often suffer from model mismatch due to the absence of nonlinear mechanical damping representation and the dynamic drift of electromagnetic parameters. To address this issue, a high-fidelity dynamic digital twin modeling method for permanent magnet synchronous motors is proposed. In the static mechanism modeling dimension, a friction energy decoupling strategy is proposed, which fundamentally overcomes the inherent conflict between steady-state accuracy and transient response from a physical logic perspective. For dynamic model updating, a distributed cascaded sensing architecture based on the multi-innovation recursive least squares and extended Kalman filter (MIRLS-MIEKF) algorithms is proposed. This architecture enables …
Scheduling Optimization For Cascaded Hybrid Flow Shop Integrating Llm And Rl,
2026
School of Computer Science and Artificial Intelligence, Lanzhou University of Technology, Lanzhou 730050, China
Scheduling Optimization For Cascaded Hybrid Flow Shop Integrating Llm And Rl, Fuqing Zhao, Bin Wu, Tianpeng Xu, Ningning Zhu, Houbin Song, Yukai Yao
Journal of System Simulation
Abstract: For the cascaded hybrid flow shop scheduling problem with AGV transportation, a multiobjective optimization model is built to minimize the makespan and total energy consumption. A hybrid algorithm framework integrating the LLM, covariance matrix adaptation evolution strategy, and RL strategy selection mechanism is proposed. LLMs are employed to generate heuristic priority rules and construct high-quality initial solutions. Covariance matrix adaption evolution strategy is adopted to optimize the search in continuous priority space, and an enhanced scalar fitness function integrating hypervolume gain and solution diversity is introduced. An RL strategy selection module based on the upper level information boundary is …
Q-Learning Enhanced Memetic Algorithm For Robust Scheduling Of Space Launch Missions,
2026
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Q-Learning Enhanced Memetic Algorithm For Robust Scheduling Of Space Launch Missions, Rui Lin, Xiaolu Liu, Yonghao Du, Mingyang Geng, Zhenshun Shi
Journal of System Simulation
Abstract: A robust optimization mathematical model and an enhanced memetic algorithm (MA) for space launch mission scheduling are proposed to address the practical challenges in space launch mission scheduling under the influence of multiple uncertainties such as equipment failure, process rework due to quality issues, and technological changes. A robust optimization scheduling model for space launch missions is built by integrating the makespan under the worst-case scenario, the average maximum makespan, and the average deviation of process start times. A Q-learning enhanced memetic algorithm (QMA) is proposed, which can dynamically select parent solutions based on the population state. Simulation experiments …
Innovative Development Of Synthetic Biology Industry From Perspective Of Deep Integration Of Four Chains,
2026
Research Institute of Sun Yat-sen University in Shenzhen, Shenzhen 518057, China; Institute of Economics, Shenzhen Academy of Social Sciences, Shenzhen 518027, China
Innovative Development Of Synthetic Biology Industry From Perspective Of Deep Integration Of Four Chains, Qin Guo, Chao Zhang
Bulletin of Chinese Academy of Sciences (Chinese Version)
Future industries represent the development direction of technological innovation and emerging industries, and are the main path to accelerate the formation of new quality productivity. As an important subsector of the future industry, synthetic biology industry is becoming a new track and new format for new quality productivity, and various regions are making cutting-edge directions such as synthetic biology industry a hot topic for future industrial layout. Based on the analysis of typical cities such as Shenzhen, Beijing, Tianjin, Shanghai, Hangzhou, and Changzhou, the current integration of the Four Chains in the synthetic biology industry is facing a four-dimensional dilemma …
Ai-Driven Drug Innovation Research: Trends, Challenges, And Countermeasures,
2026
School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
Ai-Driven Drug Innovation Research: Trends, Challenges, And Countermeasures, Tian Miao, Qi Zhou, Lianzheng Zhou, Weijun Xu, Siyuan Liu
Bulletin of Chinese Academy of Sciences (Chinese Version)
Artificial intelligence (AI) is profoundly transforming the innovation paradigm, industrial ecosystem, and business model of drug research and development. In recent years, the global AI-driven pharmaceutical market has grown rapidly. Major economies, multinational pharmaceutical enterprises, and technology giants are accelerating their strategic deployment. AI pharmaceutical industry has entered a critical stage of transitioning from proof-of-concept to commercial-application. China attaches great importance to the integrated development of AI and the biopharmaceutical industry, but it still faces challenges, including data scarcity, talent shortages, algorithmic black-boxes, regulatory lag, and weak foundations. This study proposes measures from the aspects of data supply, talent cultivation, …
