Sparse large-scale multi-objective optimization problems (SLMOPs), in which most decision variables in the Pareto-optimal solution are zero, have attracted significant attention in recent years. However, most existing algorithms struggle to accurately identify the non-zero decision variables of the Pareto-optimal solution. To address this issue, an evolutionary algorithm with dynamic grouping-based reproduction (DGREA) is proposed. The proposed DGREA first employs a probabilistic empirical score selection mechanism to evaluate variable importance. Then, decision variables are dynamically grouped by a dynamic grouping method. Finally, sparse solutions are precisely generated through a dynamic group reproduction operator. The proposed algorithm is evaluated on a benchmark suite and compared against five state-of-the-art algorithms. Experimental results demonstrate that DGREA outperforms the competing algorithms in terms of convergence and diversity, particularly in high-dimensional decision spaces. Furthermore, DGREA effectively identifies the non-zero decision variables in Pareto-optimal solutions.

Vehicular platoon, a cutting-edge technology in the realm of intelligent transportation systems, holds the promise of transforming vehicle operations on roadways. By fostering seamless communication among vehicles and leveraging advanced automation, vehicular platoon enables vehicles to travel closely together, thereby reducing aerodynamic drag, optimizing fuel consumption, and improving road safety. However, due to their open and highly dynamic characteristics, the existing platoon communication schemes face efficiency and privacy challenges. These challenges stem from a substantial overhead on the Trusted Authority (TA) and vehicle sides during platoon communication, and a lack of multi-dimensional privacy preservation (e.g., identity privacy, location privacy, attribute privacy, and reputation value privacy) for platoon vehicles. To overcome these challenges, in this paper, we propose an efficient and multi-dimensional privacy-preserving platoon communication (EMPPC) scheme. Specifically, platoon formation is cloud-assisted and relies on the location, attribute, and reputation value of vehicles. We introduce a secure reputation value ciphertext comparison (SRCC) protocol during platoon leader selection, and present an attribute verification and matching (AVM) algorithm during platoon followers matching. Theoretical analysis and simulation evaluation demonstrate that the EMPPC scheme can offer multi-dimensional privacy preservation, and it is secure and efficient for platoon communication.

Unsupervised feature selection is an important tool in data mining, machine learning, and pattern recognition. Although data labels are often missing, the number of data classes can be known and exploited in many scenarios. Therefore, a structured graph, whose number of connected components is identical to the number of data classes, has been proposed and is frequently applied in unsupervised feature selection. However, methods based on the structured graph learning face two problems. First, their structured graphs are not always guaranteed to maintain the same number of connected components as the data classes with existing optimization algorithms. Second, they usually lack strategies for choosing moderate hyperparameters. To solve these problems, an efficient and stable unsupervised feature selection method based on a novel structured graph and data discrepancy learning (ESUFS) is proposed. Specifically, the novel structured graph, consisting of a pairwise data similarity matrix and an indicator matrix, can be efficiently learned by solving a discrete optimization problem. Data discrepancy learning focuses on features that maximize the difference among data and helps in selecting discriminative features. Extensive experiments conducted on various datasets show that ESUFS outperforms state-of-the-art methods not only in accuracy (ACC) but also in stability and speed.

Federated learning (FL) facilitates collaborative model training in the autonomous Internet of Things (IoT) system while preserving the privacy of local data on IoT clients. Nonetheless, the inherent non-IID characteristic of local data leads to poor convergence of a global model. Moreover, the global model fails to satisfy the personalized task demands of all clients. To address the above issues, this article studies client grouping and local model aggregation in FL from two perspectives: 1) measure of client data distribution and 2) distribution similarity among clients. To this end, a novel confidence-based similarity-aware personalized FL algorithm (FedCS) for personalized autonomous IoT is proposed by developing three key innovations, namely, a public average confidence (PAC) measure, a client grouping strategy with dynamic sampling (CGDS), and a sequential aggregated weight (SAW) strategy. Specifically, the PAC measure utilizes a public data set on the server side to estimate the client’s data distribution, which promotes a fair estimate of distribution similarity among clients while minimizing privacy risks. The CGDS strategy focuses on distribution similarity among clients and approximates the client grouping problem as an auxiliary task selection problem in multitask learning. This strategy assigns a client into multiple groups and enables the valuable information from each client to circulate among multiple groups. The SAW strategy further incentivizes more similar clients within a group to share greater knowledge and generates an adaptive aggregated weight for each client within a group. A thorough experiment on CIFAR10 and two healthcare benchmarks shows that FedCS achieves a superior performance.

In the realm of miniature machine learning for Internet of Unmanned Aerial Vehicles (UAVs), the security concerns of machine learning models are obvious, especially when it comes to adversarial attacks. Models can become confused and their performance undermined by the introduction of meticulously crafted distortions. These attacks can even infiltrate a variety of models, bringing greater security risks. To understand how it works and mitigate its effects, our research focuses on scrutinizing the transferability of adversarial attacks in the expanding context of miniature machine learning for UAVs. In this paper, we introduce a formula help measure the transferability of adversarial attacks and explore ways to improve the transferability and effectiveness of adversarial attacks (e.g., a combination of attack techniques), and provide visulizations to vividly illustrate the repercussions of adversarial instances across a spectrum of attack intensities, helping facilitate more intuitive exploration and analysis of the results. For instance, our findings demonstrate that even subtle perturbations directed at specific attributes can lead to a significant decrease in model accuracy. We also evaluates the success rates of various attack algorithms and validates the proposed evaluation methodology for measuring transferability. And the outcomes unveiled in this study make noteworthy strides in fostering a profound comprehension of the transferability of adversarial attacks in the distinct realm of miniature machine learning for UAVs. Robust defense mechanisms, which ensure the impregnability of IoT-enabled UAV systems, can be cultivated by pinpointing the most efficacious attack strategies and evaluating their transferability.

PM2.5 concentration predictions can provide air pollution control, management, and early warning. However, the PM2.5 data with high-dimensionality, complexity, and dynamics pose a great challenge to achieve optimal prediction results. Convolutional Neural Networks (CNNs) has unique advantages in processing complex data and contributes to state-of-the-art performances. However, designing the architecture and selecting the learning rate for CNN are time-consuming and requires prior knowledge. Evolutionary algorithms, with the advantages of global convergence, ergodicity, robustness and adaptability, are the most commonly used methods to design the optimal framework for CNNs. Therefore, to improve the predictive performance of CNNs, this paper proposes an improved CNN method (EBRO-ICNN) which employs the enhanced battle royale optimization (EBRO) algorithm and proportional-derivative (PD) control. Firstly, the EBRO algorithm with multistrategy collaborative optimization is introduced, and validated by CEC2017 benchmark functions, which demonstrates EBRO strong global exploration capability, fast convergence speed, and low time complexity. Next, PD control is applied to adjust the learning rate of CNN (ICNN) dynamically, which improves the efficiency and stability of the network training process. At the same time, the ICNN model is optimized using the EBRO algorithm, which can reduce the human interference, generate the optimal framework automatically and enhance the prediction accuracy effectively. Finally, a private air quality dataset and two public datasets are utilized to evaluate the performance of the EBRO-ICNN model, considering 3 error evaluation metrics and 7 prediction comparison models. The experimental results demonstrate that the EBRO-ICNN model exhibits great accuracy and stability.

Abstract The density peaks clustering (DPC) algorithm can identify clusters with various shapes and densities in the underlying dataset. However, the DPC algorithm cannot exactly find the true quantity of clustering centers when computing the local density, and it is difficult to handle non‐convex datasets. Moreover, the DPC algorithm is difficult to identify boundary points and outliers without a reasonable allocation strategy when dealing with low‐density points. To solve these limitations, a novel deviation density peaks clustering (DeDPC) algorithm is proposed. First, the local deviation of the spatial distance of datasets with different structures is utilised to replace the local density to generate a more reasonable clustering center decision graph. Second, a threshold is defined to further divide and process low‐density points. Finally, outliers in low‐density points can be accurately found to accurately cluster the dataset. To evaluate the performance of the DeDPC algorithm, experiments are conducted on synthetic and real‐world datasets and the DeDPC is compared with other clustering methods. The DeDPC is also applied to medical image segmentation to further demonstrate its capability for medical image processing. The simulation results show that the DeDPC method has good validity and utility for both non‐convex datasets and medical image segmentation.