Hao Jiang

With the rapid development of Internet technologies, online intelligent education platforms have generated a vast amount of distributed student learning data. This influx of data presents opportunities for cognitive diagnosis (CD) to assess students’ mastery of knowledge concepts while also raising significant data privacy and security challenges. To cope with this issue, federated learning (FL) becomes a promising solution by jointly training models across multiple local clients without sharing their original data. However, heterogeneous response distributions across different schools, arising from differences in student populations, educational contexts, and learning behaviors, pose an additional challenge to client fairness in federated CD. To address this challenge, this paper proposes a fairness-aware fed erated c ognitive d iagnosis framework (FedCD) to jointly train CD models built upon a novel parameter decoupling-based personalization strategy, preserving privacy of data and achieving precise and fair diagnosis of students on each client. As an FL paradigm, FedCD trains a local CD model for the students in each client based on its local student learning data, and each client uploads its partial model parameters to the central server for parameter aggregation according to the devised innovative personalization strategy. The main idea of this strategy is to decouple model parameters into two parts: the first is used as locally personalized parameters, containing diagnostic function-related model parameters, to diagnose each client's students fairly; the second is the globally shared parameters across clients and the server, containing exercise embedding parameters, which are updated via fairness-aware aggregation, to alleviate inter-school unfairness. Experiments on three real-world datasets demonstrate the effectiveness of the proposed FedCD framework and the personalization strategy compared to five FL approaches under three CD models.

The Vehicle Routing Problem with Time Windows under Uncertain Travel Times (VRPTW-UT) is a challenging and practically significant combinatorial optimization problem. Although evolutionary algorithms (EAs) have shown potential in solving VRPTW-UT, they often struggle to balance robustness and convergence. Conventional EA approaches evaluate solutions across multiple disturbance scenes and discard those that become infeasible under any scenario. This often leads to the premature elimination of solutions that are only infeasible in a limited number of scenes, hindering the ability to effectively explore the trade-off between robustness and convergence. To address this issue, this paper proposes a Game-Theoretic Evolutionary Algorithm (GTEA) that models the search process as a game between two adversarial components: a perturbation generation part that constructs high-impact uncertainty scenes, and a robustness enhancement part that improves solutions under those critical conditions. This antagonistic process forces the population to evolve toward solutions that possess both high robustness and convergence, so that GTEA can efficiently produce solutions with high robustness and convergence. Extensive experiments on four benchmark datasets demonstrate that GTEA outperforms five state-of-the-art algorithms designed for VRPTW-UT, achieving superior convergence and robustness.

Given the immutability of biometric data, it is imperative to develop a biometric template protection method that guarantees the complete non-disclosure of any original biometric information while ensuring high recognition performance. Two mainstream approaches in biometric template protection—cancelable biometrics and biometric cryptosystems—have been widely adopted; however, the protected templates produced by these methods still contain some of the original biometric data, which can lead to privacy leakage. To address these challenges, we propose a novel framework named BioDeepHash that integrates deep hashing with cryptographic hash functions. In our approach, a deep hashing model generates consistent templates for similar biometric data from the same user, thereby eliminating intra-class variations. An application-specific XOR string is then used to achieve revocability, and finally these consistent templates are processed by cryptographic hash functions to produce protected templates that meet strict security standards. Our experimental results show that, compared with existing methods, BioDeepHash increases the average Genuine Acceptance Rate by 10.12% on the iris dataset and by 3.12% on the facial dataset, while achieving an extremely low False Acceptance Rate—0% for the iris dataset and only 0.0002% for the facial dataset.

The capacitated electric vehicle routing problem (CEVRP) has drawn much attention from researchers in the recent decade against the background of the rising electric transportation industry. Existing studies have found the CEVRP more difficult to address than the typical CVRP since the CEVRP needs to simultaneously optimize routing plans and charging decisions at a high computational budget. Given a certain routing plan, it takes much computational cost to exhaustively or approximately achieve the accurate optimal charging decision under the routing plan. This paper proposes that it is unnecessary to search for the accurate optimal charging decisions for potentially low-quality routing plans found during the CEVRP optimization, and instead obtaining acceptable charging decisions significantly reduces the computational cost and helps maintain fast convergence. A multifidelity-based ant colony optimization (MFACO) algorithm is then proposed to flexibly search charging decisions based on the potential quality of routing plans so that CEVRPs can be addressed at high efficiency. The proposed MFACO employs a low-fidelity search strategy to obtain coarse charging decisions for potentially low-quality routing plans, whereas for potentially high-quality routing plans MFACO employs three high-fidelity search strategies to local search in different regions of decision space and provide accurate optimal charging decisions. Experimental results demonstrate that the proposed multifidelity method enhances the efficiency of ACO in solving CEVRPs and the proposed MFACO significantly outperforms four state-of-the-art algorithms for CEVRPs, providing a competitive performance in terms of both solution quality and computational cost.

The Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) is a widespread real-world challenge, and numerous algorithms have been proposed to address it. However, in the context of an emerging logistics paradigm, namely the instant delivery, the performance of existing algorithms tailored for DVRPTW degrades significantly, as instant delivery allows only very limited computational time for solving DVRPTW instances. Owing to the periodic nature of customer orders, this paper proposes a data-driven evolutionary algorithm (DDEA) for solving DVRPTW under limited computation time. In the offline phase, a set of generalized solutions is derived from historical data via a dedicated evolutionary algorithm. These solutions are then directly employed in the online phase to construct high-quality solutions for new problem instances. By leveraging these precomputed generalized solutions, DDEA effectively operates within tight time constraints. Extensive experiments using synthetic and real-world data demonstrate that DDEA outperforms five state-of-the-art algorithms designed for DVRPTW under limited computation time, particularly under extremely short time constraints.

The Multi-depot Vehicle Routing Problem with Uncertain Demand (MD-VRPUD) can be modeled as a bi-level optimization problem (BLOP), because it requires both assigning customers to different depots and determining the routes for servicing customers, where the optimization of these two parts is coupled with each other. Although the bi-level evolutionary algorithm is a fitting approach for tackling the MD-VRPUD, its nested structure often leads to computational inefficiency. To this end, this paper tailors a surrogate-assisted bi-level evolutionary algorithm (SABLEA) to achieve highly efficient nested algorithms tailored for solving the MD-VRPUD. To deal with the combinatorial property of MD-VRPUD, two groups of continuous features are first extracted to help the surrogate model to effectively distinguish the superiority and inferiority of schemes. Then, a management strategy is designed to adaptively build the surrogate models in different subspaces so as to alleviate the performance bottleneck faced by the model. Finally, an adaptive computing resource allocation strategy is integrated into the lower-level optimization, to allocate more resources to promising customer assignment schemes, enabling the discovery of better routes and improving the overall accuracy of models. The comprehensive experimental results demonstrate the effectiveness of the SABLEA in handling MD-VRPUD, outperforming four existing algorithms in terms of both computational efficiency and solution quality.

An accurate wind farm model is a crucial prerequisite to ensure that the simulation results of the power system containing wind power align with reality. Hence, it is imperative to establish a system for evaluating the accuracy of wind farm models. This paper examines the fault response characteristics of wind turbines and proposes a multi-dimensional quantitative index system for assessing the equivalency of wind farm models. The entire process can be segmented into two stages: the transient period immediately following the fault occurrence and the subsequent steady-state phase. The evaluation indexes for assessing the accuracy of wind farm models are voltage dynamic response, active power dynamic response, and reactive power dynamic response. These indexes are formulated to evaluate the equivalency of wind farm models across various stages.

Large-scale grid-connection of wind power has a profound impact on the characteristics of power grids. To study the mechanism of the influence of voltage sudden changes on the transient response characteristics of double-fed wind fields is the basis of ensuring the safe and stable operation of power grids. In this paper, the expression of rotor current under symmetrical voltage sudden changes is derived, and the related factors affecting the transient response characteristics of rotor current are analyzed. Then, a detailed DFIG model is built in PSCAD, and a variety of scenarios are simulated to verify the accuracy of the proposed doubly-fed fan’s transient response characteristics under symmetrical voltage sudden changes.