Shenshen Gu

The multi-objective traveling salesman problem (MOTSP) is a representative class of multi-objective combinatorial optimization problems, with significant implications for both theoretical research and practical applications. Although deep reinforcement learning (DRL) has shown promise in solving MOTSPs, existing approaches often struggle with generalization to large-scale problem instances. To address this challenge, we propose a novel meta-deep reinforcement learning framework with preference-fused attention networks (MDRL-PFAN). This framework integrates a preference-fused mechanism to jointly encode problem instances and weight preferences into a unified feature space. Moreover, an ensemble meta-learning strategy is adopted to train the meta-model across tasks with varying scales, equipping MDRL-PFAN with robust solving and strong crossscale generalization capabilities. During inference, a lightweight fine-tuning process on small-batch adaptation tasks is employed to further enhance optimization performance. Extensive experiments on diverse MOTSP instances demonstrate that MDRL-PFAN achieves superior performance compared to classic evolutionary algorithms and state-of-the-art DRL algorithms in terms of training efficiency, solution quality, and cross-scale generalization capability.

The multiobjective traveling salesman problem (MOTSP) is a class of classical multiobjective combinatorial optimization problems (MOCOPs), attracting significant interest across various disciplines. Due to the NP-Hard nature, traditional algorithms struggle to find feasible solutions in a short period of time. To this end, this paper proposes a deep reinforcement learning algorithm based on graph weight multi-pointer network (GWMPN), which only needs to perform forward propagation on the GWMPN model to obtain the solution of MOTSP, thus significantly improving the solution efficiency. Specifically, we encode the problem instance and weight vector by means of the graph weight encoder of the GWMPN model to construct the overall encoded feature with enhanced representation capabilities. Subsequently, the encoded feature as the context vector is fed into the multi-pointer decoder to construct solutions through diversity interactions. Experimental results demonstrate that our algorithm solves the MOTSP more rapidly than classical heuristic algorithms and exhibits superior performance and generalization compared with learning-based algorithms within a reasonable time frame.

The rotary viscometer is the most commonly used device for measuring viscosity, after it is used in real applications, manufacturers need to program maintenance and add new software functions to the device. This paper designs and implements a remote upgrade system for a rotational viscometer based on the message queuing telemetry transport (MQTT) protocol and WiFi module to lower maintenance costs and enhance the convenience of updating. After receiving the firmware upgrade command and reporting packet creation information, the system receives the packet sent by the Internet of Things (IoT) platform and performs cyclical redundancy check (CRC) verification. The system uses the ESP8266 module as the communication module and uses the MQTT protocol for data transmission with the IoT platform. CRC is used to confirm the accuracy and integrity of data transfer, and if the CRC checksum is consistent, the packet is written to Flash using in application programming (IAP) technology. U ndownloaded packets are continually requested by the system until all packets are written to Flash. To minimize issues brought on by upgrade failure or other errors, the system offers breakpoint continuation and program rollback features. The system has undergone several tests to ensure its reliability and serve as a guide for remote firmware upgrades of embedded devices.

To address the limitations of traditional viscosity measurement methods, such as low intelligence and lack of remote control capabilities, this study presents a novel viscosity measurement and control system based on the FreeRTOS embedded real-time operating system. The proposed system partitions the viscosity measurement process into multiple tasks and schedules them in parallel using the FreeRTOS kernel. This architecture offers high operational efficiency, robust function expandability, and superior real-time performance compared to conventional embedded systems. Experimental results demonstrate that the system achieves high accuracy and stability in various viscosity measurement functions.

Long Short-Term Memory (LSTM) and its variants have been widely adopted in many sequential learning tasks, such as speech recognition and machine translation. The low-latency and energy-efficiency requirements of the real-world applications make model compression and hardware acceleration for LSTM an urgent need. In this paper, we first propose a weight parameter generation method based on vector construction that can make the model have a higher compression ratio and produce less precision attenuation. Furthermore, we study in detail the influence of the size of the construction vector on the computational complexity, model compression ratio and accuracy of the construction vector, in order to obtain the optimal size design interval. Moreover, we designed a linear transformation method and a convolution method to reduce the dimension of the input sequence, so that it can be applied to training sets of different dimensions without changing the size of the model construction vector. Finally, we use high-level synthesis (HLS) to deploy the obtained LSTM inference model to the FPGA device, and use the parallel pipeline operation to realize the reuse of resources. Experiments show that, compared with the block circulant matrix method, the proposed designs generated by our framework achieve up to 2 times gains for compression with same accuracy degradation, and it has an acceptable delay. With the same compression ratio, our accuracy decay is 45% of the former.

The Max-cut problem is a well-known combinatorial optimization problem, which has many real-world applications. However, the problem has been proven to be non-deterministic polynomial-hard (NP-hard), which means that exact solution algorithms are not suitable for large-scale situations, as it is too time-consuming to obtain a solution. Therefore, designing heuristic algorithms is a promising but challenging direction to effectively solve large-scale Max-cut problems. For this reason, we propose a unique method which combines a pointer network and two deep learning strategies (supervised learning and reinforcement learning) in this paper, in order to address this challenge. A pointer network is a sequence-to-sequence deep neural network, which can extract data features in a purely data-driven way to discover the hidden laws behind data. Combining the characteristics of the Max-cut problem, we designed the input and output mechanisms of the pointer network model, and we used supervised learning and reinforcement learning to train the model to evaluate the model performance. Through experiments, we illustrated that our model can be well applied to solve large-scale Max-cut problems. Our experimental results also revealed that the new method will further encourage broader exploration of deep neural network for large-scale combinatorial optimization problems.