Addressing the challenges of real-time precise localization and dynamic threat assessment of low, slow, small targets (LSS) in multi-domain, multi-dimensional environments, this paper proposes a method combining the unscented Kalman filter (UKF) with dynamic Bayesian networks (DBN). These challenges include tracking LSS targets’ unpredictable motion and assessing their threat levels in real time. A mathematical framework for real-time localization is established based on the motion characteristics of LSS targets. Two approaches are employed to deduce real-time state information: one based on UKF filtering estimates and the other on traditional single-point calculation. The DBN model is utilized for dynamic inference of the target’s threat level. Simulation results demonstrate that this method accurately captures temporal changes in threats, exhibits good robustness, and provides scientific support for rapid decision-making in modern air defense.

This study focuses on the Team Orienteering Problem with Multiple Time Windows (TOPMTW), where each vertex requires two visits. The profit of a vertex depends both on the time interval between the two visits and on the service consistency, namely whether a patient is served by the same caregiver both times. This study is motivated by some key home health care services, such as peritoneal dialysis equipment assistance, postoperative fluid injections and daily rehabilitation training. In this paper, we present a mixed-integer linear programming (MILP) model, along with an improved Adaptive Large Neighborhood Search (ALNS) algorithm. The improved ALNS algorithm incorporates a tailored time interval adjustment algorithm to further optimize the profit. Computational results demonstrate the effectiveness of the improved ALNS algorithm, highlighting its superiority over CPLEX. The incorporation of the time interval adjustment algorithm can help ALNS to obtain optimal solutions for a broader range of problem instances. The results on profits suggest that, in determining the necessity of maintaining service consistency, the influence of service duration outweighs that of the rate of profit decline across various time intervals. For services of short durations, maintaining service consistency typically leads to higher profits. Conversely, for services of long durations, service scheduling necessitates flexible adjustments to service consistency, especially when numerous conflicts of time windows arise

The centralized control architecture and programmable features of Software Defined Networking (SDN) present significant opportunities for optimizing Low Earth Orbit (LEO) satellite network performance. Nevertheless, the time-varying topology and non-uniform user distribution characteristics of LEO satellite networks lead to controller load imbalance, which necessitates adaptive controller-switch mapping mechanisms to maintain optimal load distribution between controllers. Most existing migration strategies overlook the overall network performance, resulting in sub-optimal migration quality. Moreover, they fail to address the issue of isolated nodes during migration, which adversely affects network reliability and security. To address these issues, a mathematical optimization model is formulated with the objectives of minimizing latency and achieving controller load balancing, subject to constraints such as controller capacity and intra-domain switch connectivity. To solve this model, we propose a dynamic switch migration algorithm based on deep reinforcement learning and heuristic method (DSM-DH), which comprises two phases: control relationship optimization and connectivity restoration. In the first stage, the deep reinforcement learning (DRL) framework with a multi-neural network architecture is employed, incorporating a dynamic epsilon-greedy strategy and a prioritized experience replay mechanism to comprehensively optimize control relationships while satisfying controller capacity constraints. In the second stage, the heuristic approach is used to address the isolated nodes that arise during the migration process. Without violating the controller capacity constraints, isolated switches are prioritized for migration to the controller with the lowest load, so as to minimize the disturbance to the control relationships optimized in the first stage, thereby achieving full connectivity among switches within each domain. Finally, simulation experiments are conducted to compare the DSM-DH algorithm with existing benchmark algorithms across several key performance metrics, including latency and load balancing. The results demonstrate that the DSM-DH algorithm can effectively improve network performance.

Applying software-defined networking (SDN) to low earth orbit (LEO) satellite networks and employing multiple controllers for cooperative management can significantly enhance network performance by enabling centralized control. However, SDN controller deployment in LEO satellite networks faces significant challenges due to dynamic topologies and limited network resources. To address these issues, incorporating dynamic topology, node degree, and node failure probability of LEO satellite networks as key factors, we formulate a multi-objective mathematical model for controller deployment with network delay and reliability as the objectives. The problem is proved to be NP-hard and we introduce the reinforcement learning-based genetic algorithm (RLGA) that adopts an elitist retention strategy and integrates single-point and multi-point crossover operators with multiple knowledge-based mutation operators. RLGA employs Q-learning inspired approach to adaptively select genetic operators based on historical performance metrics, which is beneficial to enhance the search efficiency and solution quality, ultimately yielding optimized controller placement schemes with superior performance characteristics. Finally, the performance of the proposed algorithm is experimentally validated, and the impact of the number of controllers and satellites on the controller deployment is analyzed. Results demonstrate that RLGA effectively reduces controller-switch latency and improves node reliability compared to other optimization algorithms. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

This study examines the practical operations of two-echelon logistics distribution systems from a holistic perspective, in which multi-commodity, supply and demand mismatches, commodity-wise split deliveries, and delivery time windows for customers are considered. Each customer demands multiple commodities, requiring timely deliveries within designated time windows. Additionally, city distribution centers (CDCs) supply different amounts of commodities to satisfy the demands of satellites, which often leads to mismatches between supply and demand. To address this issue, the multi-commodity demands of a satellite can be split and fulfilled by different city distribution centers. Consequently, we introduce a new variant of the two-echelon vehicle routing problem, referring to it as Multi-Commodity Two-Echelon Vehicle Routing Problem with Commodity-wise Split Delivery and Supply-Demand Mismatch (MC-2E-VRP-CSDSDM). We present a Mixed Integer linear programming (MILP) model and propose inequalities pertaining to vehicle dispatch order, vehicle capacity constraints, and customer access order. Furthermore, this study develops an Adaptive Large Neighborhood Search (ALNS) algorithm combined with Simulated Annealing (SA) mechanism to solve the problem. The ALNS algorithm incorporates two initialization methods that consider customer time windows and waiting times, respectively. Extensive numerical experiments were conducted to verify the effectiveness of the inequalities, and to assess the efficiency of the solution algorithm. Moreover, the average gap of two objective values obtained by CPLEX and the ALNS is 10.05%, which exhibits that the objective values obtained by the ALNS are better than those obtained by CPLEX in most instances. Using benchmark instances for the Two-Echelon Vehicle Routing Problem with Time Windows (2E-VRPTW), comparative experiments further confirmed the effectiveness of the ALNS. Computational results also demonstrated the superiority of the initialization method based on waiting times. A sensitivity analysis further reveals the impact of different removal operators, the cooling coefficient, and the selection probability of removal operators on the algorithm's performance. Furthermore, we also analyze the favorable performance of the designed greedy insertion operator and the time adjustment operator. Finally, this study offers valuable insights for managing two-echelon vehicle routing systems, providing practical guidance for logistics operations.

The time dimension critically shapes decision-making and conflict evolution in real-world scenarios. This paper extends the Graph Model for Conflict Resolution (GMCR) framework by integrating time attributes, proposing a novel Time-Sensitive GMCR (TSGMCR) methodology that supports concurrent moves by multiple decision-makers (DMs). Within TSGMCR, we define new stability concepts and implement comparative analysis. The methodology is applied to the Jakarta–Bandung high-speed railway project conflict, demonstrating its effectiveness in resolving complex real-world conflicts and identifying beneficial coalition formations.

Technology competition stands as a critical determinant of national ascendancy or decline. This paper proposes a Lifecycle-Enhanced Time Sensitive Graph Model for Conflict Resolution (LETS-GMCR) to analyze technology competition among great powers. The model defines technological structure as a temporal construct, with different decision-makers positioned in various stages of the technology lifecycle. The effective time is introduced model for evaluating unilateral moves (UMs) and unilateral improvements (UIs) and redefining stability definitions. Finally, A case study on technology competition of Global Navigation Satellite System(GNSS)-involving the U.S., Russia, China, and the EU-validates the feasibility and validity of the model.

In the graph model for conflict resolution (GMCR), option prioritization is an important approach to obtain preferences of decision makers (DMs) over a special conflict. The preference, based on ordered preference statements, is influenced by the authenticity and the order of preference statements. Owing to ambiguous evidence and incomplete information in complex real-world conflicts, it is of great difficulty to determine crisp preference over states for DMs. Some uncertain preferences have been introduced to cope with two situations that uncertain about preference statements authenticity or prioritization. The belief structure can be used to capture vagueness or unknown in subjective judgments. In this article, we propose a belief option prioritization technique by considering two situations of preference statements to elicit preferences comprehensively and efficiently. First, a belief structure is applied to describe the uncertainty of preference statement authenticity. The belief degree represents the accuracy of each preference statement and the real preference attitude of a DM. Second, a belief distribution, associated with an ordered sequence of preference statements, is used to capture the uncertainty on the prioritization of preference statements. The belief preference over feasible states can be obtained by the scoring scheme of option prioritization based on the ordered preference statements. Last, we propose an overall belief option prioritization technique by combining two uncertain situations. The application of Gisborne Lake water export conflict is utilized to illustrate the use of belief option prioritization.

This study investigates an Earth observation satellite scheduling problem for monitoring key targets' dynamics, where each target requires two observations within a reasonable time interval. The observation profit and observation effect depend on the interval between the two observations. To define the relationship between observation profits and intervals, a new profit function is introduced. Subsequently, a mixed-integer linear programming model is formulated. Furthermore, in order to efficiently address the problem, an exact branch-and-price algorithm is proposed. To improve solution efficiency, a neighborhood search algorithm is utilized to provide initial feasible solutions. In addition, the pricing problem is solved using a bidirectional label-setting algorithm, employing dynamic ng-path relaxation. To obtain integer solutions quickly, diving heuristic and matheuristic branching strategies are presented. More specifically, the diving heuristic strategy is used to obtain a lower bound at each column generation iteration. Computational results demonstrate that the proposed branch-and-price algorithm is superior to the conventional branch-and-cut algorithm used in CPLEX software package. Furthermore, when integrating the diving heuristic and matheuristic branching strategies, computational time is drastically reduced by an average of 98.7% compared to the exact branch-and-price algorithm. The practical applicability of the proposed algorithms is further validated and assessed through a real-world case study.

This paper studies the team orienteering problem (TOP), wherein each vertex requires two visits, and the service profit of each vertex depends on the time interval between these two visits. Motivated by scenarios like perishable product delivery, home health care scheduling, and earth observation satellite scheduling, the paper addresses two variants: the periodic orienteering problem with a focus on the number of days (NoDs) and the team orienteering problem with attention to a combination of time windows (CoTWs). It develops mixed-integer linear programming (MILP) models for both variants and devises exact Branch-Price-and-Cut (BPC) algorithms tailored to their block diagonal structures. Furthermore, this paper proposes two strategies to improve algorithm performance. A simplification strategy streamlines the directed graph network by removing redundant vertices and arcs without compromising optimality, thereby accelerating the solution of pricing problems. Additionally, a matheuristic algorithm is proposed to obtain integer solutions quickly. Computational results demonstrate the effectiveness of these algorithms, showcasing their superiority over CPLEX. The BPC algorithm, coupled with the simplification strategy, exhibits an average computational time reduction of 70% compared to the basic strategy. The effectiveness of the matheuristic algorithm is also confirmed. Finally, the findings presented herein offer valuable insights for refining and applying BPC algorithms.

In order to satisfy the security management requirements of combat data, such as availability, verifiability, and non-repudiation, it’s necessary to establish a unified and standardized framework for identity authentication and security guarantee. This framework should address the shortcomings of traditional application model and ensure the business data integrity of combat data management system. The blockchain has the natural characteristic of multi-party and tamper proof, which would enhance the security, reliability, and credibility of combat data. Considering the high dynamic of the battlefield environment and the hierarchical mode of data analysis, we design a basic framework for combat data management based on blockchain. A trusted method of identity authentication is further proposed based on consensus mechanism. A protection mode of data security is constructed based on state secret protection protocol, which consists of signature data and encryption protocol.Our designed framework would support the long-term, stable, and efficient operation of the combat data management system.

The strategic conflict is always an important means for countries and enterprises to maintain competitiveness in a complex environment, and it confronts with a large amount of cognitive information. The confrontation analysis can take the subjective cognitive information in a strategic conflict into consideration. The decision-making is subject to resource constraints, and the key problems should be solved first. However, due to the complexity of objective things and the fuzziness of human thinking, the current research is difficult to evaluate the key dilemmas in the conflict process. Therefore, we propose a method which is based on fuzzy linguistic assessment to evaluate the key dilemmas in the confrontation analysis. First, it analyzes the elements of the dilemma according to the generating criterion of dilemma. Second, it describes the clarity of the doubt and the gap between position and intention based on fuzzy language. Finally, it evaluates the existing dilemmas, so as to identify the key dilemmas and assist countries or enterprises to make decisions under resource constraints. The feasibility and applicability of this method are illustrated by a case study.

It is challenging to handle the non-linear power consumption model, complex workflow structures, and diverse user-defined deadlines for energy-efficient workflow scheduling in sustainable cloud computing. Although metaheuristics are very attractive to solve this problem, most of the existing work regards the problem as a black-box and ignores the use of domain knowledge. To make up for their shortcomings, this paper tailors an energy-aware intelligent scheduling algorithm (EIS) with three new mechanisms. First, we derive the optimal execution time that minimizes energy consumption for each task on a given resource. Second, based on the optimal execution time of each workflow task, the EIS distributes the workflow slack time (difference between its completion time and deadline) to reduce the voltages and frequencies of task executions for energy saving. Third, the EIS mines the idle time gaps caused by task precedence constraints to further reduce dynamic energy consumption whilst satisfying workflows’ deadline constraints. To measure the performance of the EIS, we conduct extensive comparison experiments based on actual workflow applications. The results demonstrate that the energy consumption of the EIS is much lower than that of the competitors under different deadlines, and has a faster descend rate with the evolution process.

大国博弈日趋激烈,战略博弈推演逐渐成为各国面对战略博弈问题进行评估决策的重要方法.立足战略博弈特征,提出基于对抗分析的战略博弈推演方法,从战略博弈中各推演方针对策略选项的立场、意图和质疑等认知信息描述推演场景,识别当前场景各推演方存在的冲突矛盾点,评估战略态势,并构建多阶段战略博弈推演模型.通过示例表明该方法分析战略博弈问题的可行性与适用性,可为指挥决策提供支撑.

This study examines key characteristics of cloud computing technology within the domain of federated learning, with the primary objective of exploring the principles concerning distributed computing acceleration ratio and storage input/output efficiency across various network segments. Combining the heterogeneous characteristics of cloud segments, we present a novel approach for distributed model training strategies by using the federated learning technique. This approach can keep the accuracy of the model, enhance training efficiency, and protect user privacy. The experiment demonstrates that the proposed strategy outperforms the baseline method, which does not account for variations in different segments.

Sorting solutions play a key role in using evolutionary algorithms (EAs) to solve many-objective optimization problems (MaOPs). Generally, different solution-sorting methods possess different advantages in dealing with distinct MaOPs. Focusing on this characteristic, this article proposes a general voting-mechanism-based ensemble framework (VMEF), where different solution-sorting methods can be integrated and work cooperatively to select promising solutions in a more robust manner. In addition, a strategy is designed to calculate the contribution of each solution-sorting method and then the total votes are adaptively allocated to different solution-sorting methods according to their contribution. Solution-sorting methods that make more contribution to the optimization process are rewarded with more votes and the solution-sorting methods with poor contribution will be punished in a period of time, which offers a good feedback to the optimization process. Finally, to test the performance of VMEF, extensive experiments are conducted in which VMEF is compared with five state-of-the-art peer many-objective EAs, including NSGA-III, SPEA/R, hpaEA, BiGE, and grid-based evolutionary algorithm. Experimental results demonstrate that the overall performance of VMEF is significantly better than that of these comparative algorithms.

Since both are nuclear-armed countries, the conflict arisen frequently between India and Pakistan is considered to be one of the most dangerous international territorial disputes. In this paper, we utilize the graph model for conflict resolution (GMCR) to explore the inner mechanism and possible evolution of a recent India-Pakistan Conflict in 2019. First, the modeling and analysis steps of GMCR is briefly introduced. Second, the model of the foregoing conflict is built, and a stability analysis is performed, given the actual background. After that, the evolution path of the conflict is analyzed to reveal the interactions between India and Pakistan and the role of interventions from the third party in processing the conflict. In short, this paper studies the India-Pakistan Conflict and offers the foresights not only for them but also for the third parties to resolve the disputes.

For many-objective optimization problems (MaOPs), the proportion of non-dominated solutions in a population scales up sharply with the increase in the number of objectives. Besides, for an MaOP with a fixed number of objectives, the proportion of non-dominated solutions may also grow to a high level with the progressing of the evolutionary process, sometimes even reaching 100%. Thus, a great challenge has been posed to traditional Pareto-dominance-based many-objective evolutionary algorithms (MaOEAs). To address this issue, a new fractional dominance relation is proposed to distinguish non-dominated solutions for strengthening convergence. To be special, the number of objectives on which one solution is better than the other one is considered in the fractional dominance relationship. At the same time, the objective space decomposition approach is improved with a subspace selection mechanism to maintain the population diversity. Then, two new MaOEAs referred to as FDEA-I and FDEA-II are proposed on the basis of fractional dominance relation and the improved objective space decomposition strategy. The two algorithms first use fractional dominance relation to retain some solutions with promising performance, and then the improved objective space decomposition strategy is used to maintain diversity for the obtained population. Finally, to evaluate the performance of FDEA-I and FDEA-II, extensive experiments are conducted to compare them with six state-of-the-art MaOEAs on 72 many-objective benchmark instances taken from WFG and MaF test suites. The results show that FDEA-I and FDEA-II perform significantly better than the six comparative algorithms.

Decomposition-based evolutionary multiobjective algorithms (MOEAs) divide a multiobjective problem into several subproblems by using a set of predefined uniformly distributed reference vectors and can achieve good overall performance especially in maintaining population diversity. However, they encounter huge difficulties in addressing problems with irregular Pareto fronts (PFs) since many reference vectors do not work during the searching process. To cope with this problem, this paper aims to improve an existing decomposition-based algorithm called reference vector-guided evolutionary algorithm (RVEA) by designing an adaptive reference vector adjustment strategy. By adding the strategy, the predefined reference vectors will be adjusted according to the distribution of promising solutions with good overall performance and the subspaces in which the PF lies may be further divided to contribute more to the searching process. Besides, the selection pressure with respect to convergence performance posed by RVEA is mainly from the length of normalized objective vectors and the metric is poor in evaluating the convergence performance of a solution with the increase of objective size. Motivated by that, an improved angle-penalized distance (APD) method is developed to better distinguish solutions with sound convergence performance in each subspace. To investigate the performance of the proposed algorithm, extensive experiments are conducted to compare it with 5 state-of-the-art decomposition-based algorithms on 3-, 5-, 8-, and 10-objective MaF1–MaF9. The results demonstrate that the proposed algorithm obtains the best overall performance.