Yanguang Cai

In extreme weather conditions, the use of unmanned ground vehicles (UGVs) for material distribution enhances safety. We introduce a two-echelon unmanned ground vehicle routing problem (2E-UGVRP) and proposes a hybrid Artificial Bee Colony–Wild Horse Optimizer (HABC-WHO) algorithm to solve it. In this approach, the optimal solution obtained from the Artificial Bee Colony algorithm replaces the worst solution of the Wild Horse Optimizer. To further improve the algorithm’s performance, strategies such as large neighborhood search, two-optimization (2-Opt) operation, and satellite subpath crossover are incorporated. The algorithm’s effectiveness is demonstrated through the solution of 43 benchmark instances, with performance comparisons against a Genetic Algorithm (GA), Discrete Wild Horse Optimizer (DWHO), and Discrete Artificial Bee Colony–Fixed Neighborhood Search (DABC-FNS). The results clearly show the significant superiority of the proposed algorithm. Additionally, the algorithm is applied to material distribution by two-echelon UGVs under extreme weather conditions, yielding promising results. Experimental findings indicate that the algorithm exhibits strong solving capability and high precision.

Nowadays, breast cancer is a leading cause of cancer-related mortality among women globally. Approximately 10% to 15% of breast cancer patients fail to undergo timely screening, resulting in a missed opportunity for optimal treatment. Computer-aided diagnosis (CAD) systems have been used successfully in breast cancer diagnosis. Nevertheless, current systems have encountered difficulties in achieving a high degree of accuracy, with the majority of research efforts focusing on the binary classification that distinguishes benign from malignant. Different subtypes of breast cancer require different targeted therapeutic approaches. Therefore, the precise classification of the breast cancer subtype has a major impact on treatment decisions. To improve the accuracy of breast cancer multi-classification, a novel Attention-based Mask Reconstruction Module (AMRM) is proposed to improve the performance of the model. AMRM extracts features from breast cancer histopathological images through the attention module and then performs mask reconstruction to generate reconstructed features. These reconstructed features were used in a multi-classification task to accurately classify histopathological images of breast cancer. AMRM enables the network to effectively identify background and foreground in histopathological images, reduce background interference, improve adaptability to background changes, align the features extracted by the model with the pathologist's expectations, and improve classification accuracy. Results from experiments conducted on the BreakHis dataset show that the inclusion of AMRM resulted in a significant improvement in multi-classification accuracy for the AlexNet, VGG11, ResNet-50 and Data-efficient Image Transformer (DeiT) models, reaching 88.48%, 93.40%, 96.49% and 94.10% respectively. Compared to the baseline model, accuracy increased by 8.28%, 2.11%, 1.27% and 1.26% respectively, demonstrating a significant improvement.

In humanitarian aid scenarios, the model of cumulative capacitated vehicle routing problem can be used in vehicle scheduling, aiming at delivering materials to recipients as quickly as possible, thus minimizing their wait time. Traditional approaches focus on this metric, but practical implementations must also consider factors such as driver labor intensity and the capacity for on-site decision-making. To evaluate driver workload, the operation times of relief vehicles are typically used, and multi-objective modeling is employed to facilitate on-site decision-making. This paper introduces a multi-objective cumulative capacitated vehicle routing problem considering operation time (MO-CCVRP-OT). Our model is bi-objective, aiming to minimize both the cumulative wait time of disaster-affected areas and the extra expenditures incurred by the excess operation time of rescue vehicles. Based on the traditional grey wolf optimizer algorithm, this paper proposes a dynamic grey wolf optimizer algorithm with floating 2-opt (DGWO-F2OPT), which combines real number encoding with an equal-division random key and ROV rules for decoding; in addition, a dynamic non-dominated solution set update strategy is introduced. To solve MO-CCVRP-OT efficiently and increase the algorithm’s convergence speed, a multi-objective improved floating 2-opt (F2OPT) local search strategy is proposed. The utopia optimum solution of DGWO-F2OPT has an average value of two fitness values that is 6.22% lower than that of DGWO-2OPT. DGWO-F2OPT’s average fitness value in the algorithm comparison trials is 16.49% less than that of NS-2OPT. In the model comparison studies, MO-CCVRP-OT is 18.72% closer to the utopian point in Euclidean distance than CVRP-OT.

Driven by the supply chain, suppliers, manufacturers and warehouses are working more closely together for improving service quality. However, tremendous cost may incur in the supply chain if transportation is not planned properly and efficiently, which frustrates enterprises in the intense market. In this paper, we present a model of vehicle routing problem in complex supply chain (VRPCSC) and propose an intelligent algorithm called hybrid chaotic discrete bat algorithm with variable neighborhood search for minimizing the purchase cost of materials, processing cost, and delivery cost along the path from suppliers, to manufacturers and warehouses in the vehicle routing problem. Based on the principles of bat algorithm, a discrete chaotic initialization strategy (DCIS) and a variable neighborhood search (VNS) are adopted to enhance the convergence capacity. Finally, two sets of experiments are conducted, which show that the proposed algorithm can solve the VRPCSC effectively.

: The defects in macro traffic model research are pointed out firstly. In order to remedy these defects, traffic movements at the grid intersection were analyzed, and with the basic framework of the traffic transmission model, the new macro traffic model used in the paper for control simulation and evaluation has been proposed. Secondly, the bi-level optimization control model is proposed, using minimal delay and maximal throughput as its upper objectives, and optimal traffic coordination on both sides as the lower objective. A corresponding heuristic ant algorithm is subsequently designed to solve the control model. Finally, the proposed method is tested at a suppositional road network, under three different demand scenarios, compared with Transyt-7F. The results show that the proposed method has better performance, especially under high demand scenarios.