Qiong Yang

The multiobjective flexible job shop scheduling problem (MOFJSP) seeks to jointly minimize makespan and maximum machine workload under coupled opera-tion-sequencing and machine-assignment decisions. Although the nondominated sorting genetic algorithm II (NSGA-II) is widely used for the MOFJSP, its performance can be constrained by imbalanced machine workloads, low-quality initial populations, and limited adaptation to evolving population states. This study develops a Hy-brid-Initialized Load-Aware Adaptive NSGA-II (HLA-NSGA-II) that integrates load-aware generation of machine-assignment vectors, dynamic bottleneck-aware hy-brid initialization, and multi-operator neighborhood search with closed-loop adaptive operator selection. These mechanisms respectively incorporate current workloads into machine assignment, prioritize jobs according to the evolving partial schedule, and update neighborhood-operator selection probabilities using relative hypervolume im-provement, objective-space diversity, and search stagnation. Experiments on the Ka-cem and Brandimarte benchmark sets show that the best solutions obtained by HLA-NSGA-II attain the known makespan lower bounds on all four Kacem instances. On the ten Brandimarte instances, HLA-NSGA-II achieves the lowest mean makespan on nine and the lowest or tied-lowest mean maximum machine workload on seven. Ablation studies further support the effectiveness of the proposed mechanisms. Over-all, HLA-NSGA-II achieved competitive performance on the tested instances, with workload outcomes varying across instances and higher computational cost than HSGA. This reflects the trade-off between solution quality and computational cost.

Underwater acoustic signals (UAS) in marine environments are often contaminated by noise from biological activity, ocean waves, and equipment. Under low signal-to-noise ratio (SNR) conditions, target ship-radiated components and background noise are strongly mixed, making reliable underwater detection, recognition, and communication challenging. To address this problem, this paper proposes Multi-Scale TCN, DPRNN, and Attention TasNet (MSDA-TasNet), a lightweight end-to-end source separation network for underwater ship-radiated noise. The proposed method combines time-domain convolutional encoding, a dual-path recurrent neural network (DPRNN), and a multi-scale temporal convolutional network (multi-scale TCN) to model both short-term details and long-term dependencies of UAS. To further enhance target-aware feature representation, this paper designs a novel channel–frequency attention mechanism that adaptively emphasizes target-related spectral cues. Experiments on two underwater acoustic datasets show that MSDA-TasNet achieves better separation performance than several representative baselines in terms of Signal-to-Distortion Ratio (SDR), Scale-Invariant Signal-to-Distortion Ratio (SI-SDR), SDR improvement (SDRi), SI-SDR improvement (SI-SDRi), and Segmental Signal-to-Distortion Ratio (SegSDR). The results indicate that the proposed network can improve target signal recovery under complex underwater acoustic noise while maintaining a relatively low parameter count and computational cost.

The Global Positioning System (GPS), with its accurate positioning and timing information, has become a commonly used navigation instrument for many applications. However, it is susceptible to intentional interference such as jamming and spoofing. The conventional antijamming GPS receiver fails to work in a combined jamming and spoofing attack scenario. To solve the problem, a combined antijamming and antispoofing algorithm for a GPS receiver based on an antenna array is proposed. In this method, the jamming is eliminated by subspace projection, and then a compressed sensing framework is adopted to obtain the direction of arrival (DOA) of the despreading satellite navigation signal and detect the spoofing signal. According to the DOA of the authentic and spoofing signals, the receiver uses adaptive multibeamforming to concurrently achieve the undistorted reception of the authentic satellite signal and the suppression of the spoofing. We analyse three aspects of algorithmic performance: the antenna array direction diagram, the spoofing detection and the acquisition results. The simulation results and their analysis preliminarily show that the proposed method can detect and suppress GPS jamming and spoofing effectively.