In some real-world optimization problems, the parameters of an objective function may be expressed as intervals, such as the benefit of a project and the driving speed of a robot. An optimization problem involving interval parameters and multiple conflicting objectives is termed as a multi-objective optimization problem with interval parameters (IMOP). Few studies have addressed IMOPs compared to deterministic multi-objective optimization at present. In addition, the uncertainty involved in the problems raises higher demands on the diversity and efficiency of an algorithm. Therefore, an adaptive reference vector-based interval multi-objective evolutionary algorithm in the framework of MOEA/D (IMOEA/D) was proposed in this paper. Firstly, an IMOP is decomposed into a number of sub-problems with interval parameters by setting an interval-valued reference point and constructing interval-valued scalar functions. Following that, an ensemble scheme for evaluating interval individuals is developed via the rank sum ratio method. Finally, reference vectors are adaptively adjusted based on the interval crowding distance to enhance the distribution of a population. The proposed IMOEA/D was tested on 20 benchmark IMOPs as well as a scheduling problem of underwater wireless sensor networks, and compared with four state-of-the-art interval MOEAs. The empirical results demonstrate its superior performance and strong competitiveness.

Water quality monitoring is crucial for ecological protection and water resource management. However, traditional monitoring methods suffer from limitations in temporal, spatial, and spectral resolution, which constrain the effective evaluation of urban rivers and multi-scale aquatic systems. To address challenges such as ecological heterogeneity, multi-scale complexity, and data noise, this paper proposes a deep learning framework, TL-Net, based on unmanned aerial vehicle (UAV) hyperspectral imagery, to estimate four water quality parameters: total nitrogen (TN), dissolved oxygen (DO), total suspended solids (TSS), and chlorophyll a (Chla); and to produce their spatial distribution maps. This framework integrates Transformer and long short-term memory (LSTM) networks, introduces a cross-temporal attention mechanism to enhance feature correlation, and incorporates an adaptive feature fusion module for dynamically weighted integration of local and global information. The experimental results demonstrate that TL-Net markedly outperforms conventional machine learning approaches, delivering consistently high predictive accuracy across all evaluated water quality parameters. Specifically, the model achieves an R² of 0.9938 for TN, a mean absolute error (MAE) of 0.0728 for DO, a root mean square error (RMSE) of 0.3881 for total TSS, and a mean absolute percentage error (MAPE) as low as 0.2568% for Chla. A spatial analysis reveals significant heterogeneity in water quality distribution across the study area, with natural water bodies exhibiting relatively uniform conditions, while the concentrations of TN and TSS are substantially elevated in aquaculture areas due to aquaculture activities. Overall, TL-Net significantly improves multi-parameter water quality prediction, captures fine-scale spatial variability, and offers a robust and scalable solution for inland aquatic ecosystem monitoring.

Vehicular ad-hoc networks (VANETs) enable communication among vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) with a dedicated short range communications (DSRC). In VANETs, the real-time trajectory prediction service can be provided by roadside units (RSUs) using data collected from vehicles. This service is important for the prevention of traffic accidents. Due to the openness of the communication in VANETs, the transmitted data is vulnerable to illegal users' attacks that results in the leakage of vehicles' data. To address the above concerns, a privacy-preserving multi-party computation scheme with secret sharing is proposed for vehicle trajectory prediction in VANETs. The proposed scheme is designed to protect the privacy of transmitted vehicle data during trajectory prediction. Note that four distributed interactive protocols of $\boldsymbol{(\cdot)}$ , $\boldsymbol{SecExp( \cdot)}$ , $\boldsymbol{SecRec(\cdot)}$ and $\boldsymbol{SecCmp(\cdot)}$ are introduced to carry out the computation of nonlinear logarithm, exponent, reciprocal and comparison functions in the proposed scheme. The aforementioned interactive protocols facilitate the distributed deployment of the Adaboost ensemble learning algorithm among multiple RSUs, while ensuring the privacy of vehicle data using secret sharing simultaneously. Security analysis proves that the proposed scheme can effectively protect the privacy of vehicle data. Performance analysis demonstrates that the proposed scheme has a minimal impact on the prediction accuracy of the Adaboost algorithm compared to the differential privacy (DP)-based scheme. Furthermore, the low latency of the proposed scheme allows for efficient trajectory prediction, as demonstrated by the runtime of the proposed scheme on test set.