Medical gene expression feature selection is challenged by the high-dimensional small-sample regime and strong co-expression redundancy, which often yields unstable subsets and brittle trade-offs between predictive performance and compactness. This paper proposes a two-stage framework, Class-guided Joint Hybrid Multi-objective Optimizer (CJHMO), that integrates structural candidate generation with wrapper-based multi-objective optimization. In stage one, a Multi-Scale Co-Expression Attention Network (MSCANet) constructs correlation graphs under multiple thresholds and extracts connected components as co-expression modules. Each module is summarized by its eigengene (the first principal component), and the correlation ratio is used to quantify the association between eigengenes and class labels, producing supervised module scores. These scores are converted into attention weights and propagated to genes for candidate ranking and screening. In stage two, we develop a Dual-Population Heterogeneous Multi-Objective optimizer (DPHMO), where a decomposition-based population emphasizes Pareto-front coverage and global exploration, while an elite-guided particle swarm focuses on local exploitation and refinement. The two populations share an external elite archive (EP) for cross-population information exchange and non-dominated solution maintenance, jointly minimizing classification error rate and feature selection rate. Experiments on multiple public benchmarks with several classifiers demonstrate that CJHMO achieves superior performance-compression trade-offs over representative multi-objective baselines, with improved Pareto quality reflected by HV and IGD.

The marine vibrators (MVibs) have become indispensable in marine industrial exploration due to its controllable energy output, high repeatability, and environmental compatibility. MVib-based blended acquisition improves exploration efficiency by reducing sampling duration and operational costs, but it introduces significant challenges in source separation due to blending noise. Since the subsequent processing method of MVibs require precorrelation data, this article presents a deblending framework for precorrelation MVib data by integrating a prior network into the inversion framework. To address the scarcity of labeled MVib data for deep learning, a data augmentation strategy is proposed in which natural images are converted into seismic-like data using band-pass filtering to train the prior network. This approach enhances model generalization and alleviates the limitations of MVib datasets. The proposed framework is validated through multi-MVib simulations and large scale open sea trials. Experimental results demonstrate that the method effectively suppresses blending noise while preserving key signal features, outperforming conventional deblending techniques and demonstrating strong practical value in real-world marine exploration.

Early detection of colorectal polyps is crucial for clinical screening and cancer prevention, where accurate and efficient automatic segmentation plays a pivotal role. However, colonoscopy images often suffer from low contrast, blurred boundaries, and scale variations, making segmentation challenging. Existing encoder-decoder networks (e.g., U-Net) suffer from asymmetric supervision and feature redundancy, which in turn lead to semantic inconsistency and loss of fine details. While deeper or hybrid designs alleviate these issues, their high complexity and computational burden limit feasibility in real-time clinical practice. To address these challenges, we propose a lightweight segmentation framework, Hierarchical Contextual Attention Network (HCA-Net), consisting of the Redundancy-Suppressed Dual-Path Downsampling (RS-DPD) module and the Boundary-Aware Semantic Alignment Upsampling (BA-SAU) module, applied to the encoder and decoder, respectively. RS-DPD suppresses redundancy while preserving fine-grained details through a dual-path design, whereas BA-SAU leverages cross-layer contextual attention to enforce semantic consistency and enhance boundary sensitivity. Both modules are built upon our proposed Hierarchical Contextual Attention (HCA) mechanism, which combines convolutional projection with pooling-based compression to achieve efficient global modeling and accurate local boundary restoration. In addition, a composite boundary-aware loss function is designed to improve pixel-level accuracy, structural consistency, and robustness in low-contrast and boundary-ambiguous regions. Extensive experiments on public colorectal polyp datasets demonstrate that HCA-Net achieves state-of-the-art (SOTA) segmentation accuracy with significantly improved efficiency, while maintaining robustness under low-contrast and blurred-boundary conditions.

Few-shot Class-incremental Pill Recognition (FSCIPR) aims to develop an automatic pill recognition system that requires only a few training data and can continuously adapt to new classes, providing technical support for applications in hospitals, portable apps, and assistance for visually impaired individuals. This task faces three core challenges: overfitting, fine-grained classification problems, and catastrophic forgetting. We propose the Well-Prepared Few-shot Class-incremental Learning (WP-FSCIL) framework, which addresses overfitting through a parameter-freezing strategy, enhances the robustness and discriminative power of backbone features with Center-Triplet (CT) loss and supervised contrastive loss for fine-grained classification, and alleviates catastrophic forgetting using a multi-dimensional Knowledge Distillation (KD) strategy based on flexible Pseudo-feature Synthesis (PFS). By flexibly synthesizing any number of old-class features, the PFS strategy resolves the issue of insufficient samples in the KD process, enabling Response-based KD (KD1) and Relation-based KD (KD2) to comprehensively preserve old knowledge. The effectiveness of WP-FSCIL has been validated through experiments conducted on two publicly available pill datasets. These experiments show that WP-FSCIL outperforms existing state-of-the-art methods, demonstrating its superior performance.

Accurate brain tumor segmentation from multimodal MRI is critical for clinical diagnosis and treatment planning. However, effectively leveraging the complementary information across different modalities remains a significant challenge due to modality-specific noise, information redundancy and inherent model uncertainty. To tackle these challenges, we propose a Disentangled Fusion Network (DFuse-Net) that integrates disentangled feature fusion with contrastive and consistency-aware learning to enable reliable multi-modal brain tumor segmentation. Our method first explicitly disentangles modality-shared and modality-specific feature representations. Then, a Disentangled Feature Fusion Module (DFFM) is proposed to effectively integrate modality-shared and modalityspecific feature representations. In addition, a contrastive-aware learning scheme is employed to enhance feature discriminability, while a consistency-aware learning strategy is applied to enforce structural coherence across modalities. Moreover, Monte Carlo dropout is applied during inference to generate voxelwise aleatoric and epistemic uncertainty maps, enhancing the robustness of segmentation. Extensive experiments on the BraTS datasets demonstrate that DFuse-Net achieves superior segmentation accuracy and reliability compared to the state-of-the-art methods.

This paper proposes an innovative optimization framework, Dynamic Graph Neural Evolution (DGNE), integrating Graph Neural Networks (GNNs) with Evolutionary Algorithms (EAs). Building on the foundation of Graph Neural Evolution (GNE), DGNE introduces a dynamic filtering mechanism and adaptive Gaussian sampling functions to dynamically adjust the population distribution during the optimization process, achieving a balance between global exploration and local exploitation. By emphasizing high-frequency information in the early stages to enhance population diversity and low-frequency information in the later stages to promote convergence, DGNE effectively optimizes the search process. Experiments were conducted on the CEC2017 benchmark suite across 30, 50, and 100 dimensions, comparing DGNE with advanced algorithms (LSHADE, LSHADE_cnEpSin, MadDE, SaDE, EA4eig) and classic algorithms (DE and CMA-ES). Statistical analyses using the Wilcoxon rank-sum test and Friedman mean rank test demonstrate that DGNE achieves the best average ranking in 50 and 100 dimensions and ranks third in 30 dimensions. However, it achieves the highest overall average performance ranking across all dimensions, showcasing its stability and significant advantages in different scenarios. While its performance in low-dimensional tasks is slightly less competitive compared to high-dimensional ones, DGNE still exhibits strong competitiveness. Additionally, we explored the impact of population size. DGNE was evaluated across population sizes of 20, 30, 50, and 100. The results highlight DGNE’s robustness, maintaining competitive rankings across all population sizes, with top rankings for smaller population sizes (20 and 30) and strong results at larger sizes (50 and 100). These findings confirm DGNE’s adaptability to varying configurations and further validate its effectiveness as a robust optimization framework. Overall, DGNE demonstrates great potential as an optimization method, offering a promising direction for further research and applications in artificial intelligence and optimization fields. Its ability to remain competitive across diverse tasks and configurations underscores its versatility and scalability.

The gradient-based meta-learning algorithm gains meta-learning parameters from a pool of tasks. Starting from the obtained meta-learning parameters, it can achieve better results through fast fine-tuning with only a few gradient descent updates. The two-layer meta-learning approach that shares initialization parameters has achieved good results in solving few-shot learning domain. However, in the training of multiple similar tasks in the inner layer, the difficulty and benefits of the tasks have been consistently overlooked, resulting in conflicts between tasks and ultimately compromising the model to unexpected positions. Therefore, this paper proposes a task-adaptive selection metalearning algorithm called TSML. Specifically, we construct a task selection trainer to assess the difficulty of tasks and calculate their future benefits. Designing more optimal training strategies for each task based on difficulty and benefit, altering the current compromise in multi-task settings, and balancing the impact of tasks on meta-learning parameters. Additionally, the outer meta-parameter updating method for traditional meta-learning has been adjusted, enabling the meta-parameters to attain a better position. By doing so, we can rapidly improve the generalization and convergence of the meta-learning parameters on unknown tasks. Experimental results indicate a 2.1% improvement over the base model in the 4-conv setting, with a more pronounced effect as the neural network is progressively complexified, reaching a 4.1% improvement in resnet12.

Hyperspectral remote sensing image feature selection enhances the efficiency of further applications by extracting band information. However, it is challenging to optimize and extract the spectral relationship information of the entire hyperspectral image using traditional methods and solidified spectral representation strategies. As a result, band selection often leads to locally optimal candidate solutions. For instance, when applied to downstream classification tasks, the selected bands typically exhibit issues such as poor information separability, high spectral correlation, and missing information. This paper proposes a new HSIAO_BS framework based on Jeffries-Matusita distance (JM) and an evolutionary algorithm to obtain an excellent subset of bands for hyperspectral remote sensing image feature selection addressing downstream classification tasks. The research problem is modeled as a solution space with effective inter-spectral relationship representation. The HSIAO_BS framework designs an adaptive band encoding mechanism and a feature relationship representation based on JM distance to construct this space. Additionally, the key optimized search method in HSIAO_BS is the improved HSIAO. This evolutionary algorithm combines differential crossover and attenuating mutation strategies to enhance the balance between global exploration and local exploitation capabilities, while also targeting to improve the preference for band selection. The reliability, validity, and stability of the HSIAO_BS framework are verified through a series of performance test experiments conducted on three hyperspectral remote sensing image datasets to support downstream classification tasks.

Vision-based physiological monitoring is an emerging technology that enables a more convenient access of cardiovascular health status in many medical industrial applications. This article aims to achieve efficient and accurate identification of pulse waveforms by proposing a weighted temporally consistent 3-D (WTC3D) convolution, in which a spatial weight template is incorporated between the spatial and temporal kernels as a constraint for the temporal kernel. WTC3D employs a temporal kernel to keep temporal consistency and a spatial weight template to impose spatial diversity during the remote photoplethysmography (rPPG) feature learning. A WTC3D-based network with a hybrid loss function is designed for pulse prediction. Experiments on three datasets demonstrate the effectiveness of the proposed approach. By considering the temporal propagation characteristics of the pulse signal in the video, WTC3D convolution not only enables efficient pulse feature learning, but also advances the deployment of rPPG networks on source-limited Internet of medical things devices.

Capacitated Vehicle Routing Problem (CVRP) is a critical challenge in logistics optimization, which directly impact operational costs and service efficiency. While quantum differential evolution (QDE) algorithm offers potential advantages in solving combinatorial optimization problems, its application in CVRP is still limited due to the premature convergence, poor search capability and stagnation. To address these limitations, a novel multi-strategy QDE algorithm with cooperative co-evolution (CC) framework and hybrid local search strategy, namely MSCFLQDE is proposed to effectively solve the CVRP. Firstly, a new multi-population strategy with CC framework is designed to solve each sub-CVRP for enabling parallel optimization and preserving global constraints. Then an adaptive differential mutation mechanism is developed to balance the exploration and exploitation and accelerate the convergence. Thirdly, a new quantum rotation mode with the sorting coding rule is designed to adjust the search direction and reduce stagnation. In the later stage, a hybrid local search strategy is proposed to dynamically eliminate the redundant nodes and intersections. Finally, the experiment results on the five CVRPs (set A, set B, set P, set E, and set G) demonstrate that the MSCFLQDE has better search ability, higher convergence and stronger stability by comparing with the state-of-the-art algorithms(such as CCDE, CCDE-D, CCDE-R, CCDE-S, HGS, BILA, AGA-ES and TAMLS and so on), which achieves 5.53% shorter distances for P51_K10 by comparing with AGA-ES.

Recently, the development of artificial intelligence technologies has accelerated advancements in threshold segmentation techniques based on intelligent optimization algorithms, which have demonstrated good performance across various types of medical images. However, research specifically focused on the segmentation of Prostate Cancer Pathology Image (PCPI) remains limited. To fill this gap and improve the efficiency of prostate cancer diagnosis, this study proposes a threshold segmentation scheme based on intelligent optimization algorithms, MOLMVO-Rényi, specifically designed for PCPI. This scheme first enhances the optimization capabilities of the multi-verse optimizer algorithm using multiple strategies (multi-population topological structure and orthogonal learning) to propose MOLMVO. Then it utilizes MOLMVO to optimize the thresholds for image segmentation, aiming to maximize the Rényi entropy of the segmented images. Experimental validation on 67 privately collected PCPIs indicates that the proposed scheme shows significant competitive advantages, providing strong support for the early diagnosis and treatment of prostate cancer.

Deep learning has made significant advancements and breakthroughs in medical image recognition. However, the clinical reality is complex and multifaceted, with patients often suffering from multiple intertwined diseases, not all of which are equally common, leading to medical datasets that are frequently characterized by multi-labels and a long-tailed distribution. In this paper, we propose a method involving label decoupling and reconstruction (LDRNet) to address these two specific challenges. The label decoupling utilizes the fusion of semantic information from both categories and images to capture the class-aware features across different labels. This process not only integrates semantic information from labels and images to improve the model's ability to recognize diseases, but also captures comprehensive features across various labels to facilitate a deeper understanding of disease characteristics within the dataset. Following this, our label reconstruction method uses the class-aware features to reconstruct the label distribution. This step generates a diverse array of virtual features for tail categories, promoting unbiased learning for the classifier and significantly enhancing the model's generalization ability and robustness. Extensive experiments conducted on three multi-label long-tailed medical image datasets, including the Axial Spondyloarthritis Dataset, NIH Chest X-ray 14 Dataset, and ODIR-5K Dataset, have demonstrated that our approach achieves state-of-the-art performance, showcasing its effectiveness in handling the complexities associated with multi-label and long-tailed distributions in medical image recognition.

A prevalent cancer, skin cancer, requires precise segmentation for effective diagnosis and treatment. Despite the availability of numerous effective, lightweight neural network models, there remains a significant gap in their application in resource-limited settings, particularly in mobile healthcare. This study aims to bridge this gap by developing a model combining high efficiency and robust performance in such environments. This study introduces the Dynamic Spatial Group Enhanced Network (DSEUNet), significantly reducing the computational loads and parameter quantities while ensuring competitive segmentation accuracy . Our essential contribution is developing a model that excels in segmentation accuracy and addresses computational and parameter efficiency challenges in mobile healthcare applications. In extensive trials on the ISIC2017 and ISIC2018 datasets, DSEUNet not only surpasses traditional models like UNet in accuracy but does so with far fewer parameters and reduced computational demand. This research contributes to developing more accessible and efficient tools for medical image analysis, particularly in resource-constrained environments. As far as we know, this is the first model with 23.306 KB parameters and 9.497 M FLOPs.

Hyperspectral image (HSI) spectral-spatial joint feature extraction methods generally suffer from low feature retention and weak spatial-spectral dependence, which will lead to single class feature confrontation (SCFC). To solve this problem, an unsupervised multivariate feature fusion network (M3FuNet) is developed in this paper. In M3FuNet, multiscale supervector matrix correction (MSMC) and multiscale random convolution dispersion (MRCD) is used as the spectral and spatial feature extraction method, and the feature retention of spectral and spatial features is improved to achieve feature calibration by feature fusion and decision fusion, called multivariate feature fusion. The MSMC is employed to correct supervector matrix to reduce the intraclass variance in superpixel homogeneous regions, overcome the phenomenon of supervector block drift (SvBD). The MRCD uses random convolution and gaussian smoothing to extract deep spatial features. Due to the similar feature representation ability of the MSMC and MRCD, the obtained spectral-spatial joint features have high feature retention and strong spectral-spatial dependence. Finally, this multivariate feature fusion network is used for realizing classification of HSI. Three commonly HSI datasets are used to validate the effectiveness of the M3FuNet. The experiment results show that the M3FuNet has more superior performance by comparing with several state-of-the-art HSI classification methods. The code of the proposed M3FuNet is available at http://github.com/aichou233/M3FuNet.

In the field of Earth observation and computer vision, oriented object detection for remotesensing images is a crucial task that aims to locate objects more accurately in complex scenes containing a large number of densely arranged, large aspect ratio, and arbitrarily oriented objects. Although recently proposed methods have achieved remarkable performance, there are still several challenges to address: 1) interference from complex backgrounds, 2) imbalanced and mismatched label assignments caused by tiny objects and objects with large aspect ratios, and 3) misalignment between the tasks of classification and localization. In this article, we propose a frequency-assisted dynamic learning network (FADL-Net) to overcome the crucial challenges. Concretely, we introduce a spatial-spectral feature pyramid network to adaptively capture global long-range dependency feature representations containing various frequency domains. Meanwhile, to produce more reliable training samples for objects with extreme shapes, we design a geometric aware dynamic label assignment to dynamically mitigate the imbalance and mismatch in label assignment in a coarse-to-fine manner, thereby achieving more stable optimization during the training process. Moreover, we propose a joint-learning rotated quality loss that addresses the inconsistency between classification and localization by dynamically adapting the weights of different samples in the training stage. Extensive experiments on several public remote sensing datasets demonstrate that our method performs favorably against state-of-the-art detection approaches.

Semisupervised object detection (SSOD) has garnered significant interest for its capability to enhance the detection performance by leveraging large amounts of unlabeled data. However, current SSOD methods primarily focus on detecting horizontal objects, with little research devoted to the detection of arbitrary-oriented objects in remote sensing images. Drawing inspiration from this limitation, this article proposes a semisupervised oriented object detection framework (S$^{2}$O-Det) to reduce annotation costs while improving detection performance in a semisupervised manner. Initially, the proposed task-consistent learning aims to alleviate the inconsistencies between classification and localization, which provides consistent confidence for the pseudolabels. Subsequently, the introduced coarse-to-fine sample mining employs dense prediction for pseudolabel assignment, adopting a divide-and-conquer approach to independently identify consistent and reliable labels for both classification and localization tasks. Finally, a probabilistic distillation loss ensures the harmonization of the probability distributions across the teacher and student feature domains, thereby reciprocally enhancing the learning competencies. Experimental results on the DOTA-v1.0 and DOTA-v1.5 datasets demonstrate that S$^{2}$O-Det achieves promising performance across different labeling ratios.

In this paper, a large-scale multi-objective gate assignment model is constructed by considering the flight international and domestic attributes, task type, airline affiliation, and aircraft type. Then a multi-objective quantum-inspired evolutionary algorithm based on decomposition mechanism, namely MOQEA/D is developed to solve the constructed model effectively. Specifically, a new decomposition mechanism is designed to decompose the multi-objective GAP into several single-objective sub-GAPs. Each quantum bit string solves a single-objective sub-GAP independently. And a new optimal crossover strategy is proposed to limit the randomness of observation operations and maximize the preservation of excellent genes to further improve the optimization performance. Finally, the multi-objective knapsack problem and the multi-objective GAP are selected to verify the effectiveness of the MOQEA/D. The experiment results demonstrate that the MOQEA/D can effectively solve large-scale multi-objective knapsack problem and obtain ideal gate assignment results. It takes on very significance and application value in solving complex optimization problems.

Functional connectivity (FC) networks based on resting-state functional magnetic imaging (rs-fMRI) are reliable and sensitive for brain disorder diagnosis. However, most existing methods are limited by using a single template, which may be insufficient to reveal complex brain connectivities. Furthermore, these methods usually neglect the complementary information between static and dynamic brain networks, and the functional divergence among different brain regions, leading to suboptimal diagnosis performance. To address these limitations, we propose a novel multi-graph cross-attention based region-aware feature fusion network (MGCA-RAFFNet) by using multi-template for brain disorder diagnosis. Specifically, we first employ multi-template to parcellate the brain space into different regions of interest (ROIs). Then, a multi-graph cross-attention network (MGCAN), including static and dynamic graph convolutions, is developed to explore the deep features contained in multi-template data, which can effectively analyze complex interaction patterns of brain networks for each template, and further adopt a dual-view cross-attention (DVCA) to acquire complementary information. Finally, to efficiently fuse multiple static-dynamic features, we design a region-aware feature fusion network (RAFFNet), which is beneficial to improve the feature discrimination by considering the underlying relations among static-dynamic features in different brain regions. Our proposed method is evaluated on both public ADNI-2 and ABIDE-I datasets for diagnosing mild cognitive impairment (MCI) and autism spectrum disorder (ASD). Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art methods. Our source code is available at https://github.com/mylbuaa/MGCA-RAFFNet.

The main objective of this study is to propose a cyber–physical system (CPS)-based person reidentification (P-ReID) framework for smart surveillance. The Internet of Things (IoT)-based interconnected vision sensors in smart cities are considered essential elements of a CPS, and contribute significantly to urban security. However, the reidentification of targeted persons using emerging edge AI techniques still faces certain challenges. To improve efficiency at the edge and overcome the traditional sensing of video cameras, we employed an AI-based P-ReID framework for CPS that is functional in IoT environments. In addition, we present dual attention dilated network (DADNet), which integrates an energy-efficient convolutional neural network (CNN) with a self-attention module to substantially improve the person matching probability. Furthermore, we applied dual feature fusion to intelligently integrate discriminative and robust features using early and late fusion strategies that allow DADNet to significantly consider the foreground and marginally utilize the background information. Furthermore, we impose diversity orthogonality regularization over several CNN layers, which boosts the performance of DADNet, resulting in an appropriate usage over IoT networks. A comprehensive set of ablation studies, comparison with other state-of-the-art approaches, and a time complexity analysis confirm the strength of our DADNet for reidentification tasks in AI-enabled IoT settings that are well suited for a CPS.

Remote photoplethysmography (rPPG) has been an active research topic in recent years. While most existing methods are focusing on eliminating motion artifacts in the raw traces obtained from single-scale region-of-interest (ROI), it is worth noting that there are some noise signals that cannot be effectively separated in single-scale space but can be separated more easily in multi-scale space. In this paper, we analyze the distribution of pulse signal and motion artifacts in different layers of a Gaussian pyramid. We propose a method that combines multi-scale analysis and neural network for pulse extraction in different scales, and a layer-wise attention mechanism to adaptively fuse the features according to signal strength. In addition, we propose spatial-temporal joint attention module and channel-temporal joint attention module to learn and exaggerate pulse features in the joint spaces, respectively. The proposed remote pulse extraction network is called Joint Attention and Multi-Scale fusion Network (JAMSNet). Extensive experiments have been conducted on two publicly available datasets and one self-collected dataset. The results show that the proposed JAMSNet shows better performance than state-of-the-art methods.

The goal of unpaired image-to-image translation is to learn a mapping from a source domain to a target domain without using any labeled examples of paired images. This problem can be solved by learning the conditional distribution of source images in the target domain. A major limitation of existing unpaired image-to-image translation algorithms is that they generate untruthful images which are overcolored and lack details, while the translation of realistic images must be rich in details. To address this limitation, in this article, we propose a random reconstructed unpaired image-to-image translation (RRUIT) framework by generative adversarial network, which uses random reconstruction to preserve the high-level features in the source and adopts an adversarial strategy to learn the distribution in the target. We update the proposed objective function with two loss functions. The auxiliary loss guides the generator to create a coarse image, while the coarse-to-fine block next to the generator block produces an image that obeys the distribution of the target domain. The coarse-to-fine block contains two submodules based on the densely connected atrous spatial pyramid pooling, which enriches the details of generated images. We conduct extensive experiments on photorealistic stylization and artistic stylization. The experimental results confirm the superiority of the proposed RRUIT.

Optimal Transport (OT) has become a powerful tool to compare probability distributions. However, it suffers from a severe computational burden for high dimensional and continuous distributions. To this end, we develop two novel methods for the Kantorovich and Monge formulations, which are the fundamental problems in OT. First, we learn the optimal joint distribution in the Kantorovich formulation and propose an algorithm, namely Cop-OT, which transforms the primal objective of the Kantorovich problem into a tractable objective with respect to the copula parameter. Second, based on the copula formulation of the joint distribution, we learn the optimal map in the Monge problem and propose an algorithm, namely Map-OT, which describes the optimal map using a parameterized function estimated by approximating the barycentric projection of the optimal joint distribution and then obtains a tractable objective with respect to parameters of interest. Both of them can be solved by stochastic optimization with a stable optimizing process. Empirical results demonstrate that Cop-OT and Map-OT can gain more accurate approximations of the Kantorovich and Monge problems compared with the baseline methods.

Grey wolf optimization (GWO) is a widely used meta-heuristic method. It has limited searching potential when solving the majority of function optimization problems. This paper proposes a new variant of GWO, named SCGWO, which combines GWO with an improved spread strategy and a chaotic local search (CLS) mechanism to overcome these performance limitations. In detail, a spread strategy is introduced into the basic GWO to change the search agent's ability to avoid the local optima, the global exploration capability, and the individual movement's randomness. Then, a CLS mechanism is adopted to accelerate the convergence rate of the evolving agents. This method's effectiveness is illustrated by comparing the proposed SCGWO method with various algorithms, including seven GWO variants and eight well-known state-of-the-art algorithms on a comprehensive set of benchmark functions with the type of unimodal, multimodal, and composition functions. The experimental results confirmed that the established SCGWO algorithm has apparent advantages in processing unimodal, multimodal, and composition functions. Additionally, the proposed algorithm was utilized for finding the approximate optimal feature subset when applied to the feature selection problems on a set of 32 real-world data sets from the UCI machine learning repository. The results show that the binary variant also reveals a very competitive performance in dealing with feature selection. Our findings and analysis suggest that the proposed method can be a very suitable tool for realizing the optimal solutions to global optimization and wrapper-based feature selection tasks.

Gradient-based optimizer (GBO) is a metaphor-free mathematic-based algorithm proposed in recent years. Encouraged by the gradient-based Newton's method, this algorithm combines with population-based evolutionary methods. The disadvantage of the traditional GBO algorithm is that the global search ability of the algorithm is too strong, and the local search ability is too weak; accordingly, it is difficult to obtain the global optimal solution efficiently. Therefore, a new improved GBO algorithm (GOMGBO) is developed to mitigate such performance concerns by introducing a Gaussian bare-bones mechanism, an opposition-based learning mechanism, and a moth spiral mechanism enhanced GBO algorithm. The proposed GOMGBO has been compared against many famous methods and improved variants on 30 benchmark functions. The experimental results show that GOMGBO has apparent advantages in convergence speed and precision. In addition, this paper analyzes the balance and diversity of the GOMGBO algorithm and compares GOMGBO with other algorithms on several engineering problems. The experimental results show that the GOMGBO algorithm is also better than the competitive algorithm in engineering problems. This study uses the GOMGBO algorithm to optimize kernel extreme learning machine (KELM), and a new GOMGBO-KELM model is proposed. The model is used to deal with four clinical disease diagnosis problems. Compared with GBO-KELM, back propagation neural network algorithm, and other models, comparative experiments show that GOMGBO-KELM has high performance in dealing with practical cases. We invite the community to investigate further our method for solving problems more efficiently with reasonable speed and efficiency. Readers of this study can refer to https://aliasgharheidari.com for any guidance about the proposed GOMGBO method.

With the increasing demand for security in many sectors, such as defense and health systems, developing secure Internet of Things (IoT) networks is a matter of great urgency. Looking at a potential solution for secure IoT systems, we investigate the physical layer security of cooperative nonorthogonal multiple access (NOMA) systems. After decoding information signal, the idea that a strong IoT node can serve as a relay node for other weak IoT nodes in enhancing their signal reception reliability, is known as cooperative NOMA. We consider both single-antenna and multiantenna aided transmission scenarios, where the base station (BS) communicates with two IoT nodes of different strengths. In the multiantenna scenario, artificial noise (AN) is generated at the BS and the strong IoT node for improving the security of the system. In order to characterize the secrecy performance, we derive new exact expressions of the security outage probability for both the IoT nodes under both the single-antenna and multiantenna aided scenarios. For the single-antenna scenario, we show that the power optimization at the BS and the strong IoT node can enhance the secrecy performance to some extent. For this case, we further study the secrecy diversity order of the overall system, which is mainly determined by the IoT node with the worse channel condition. For the multiantenna scenario, we derive the asymptotic secrecy outage probability (SOP) when the number of antennas tends to infinity. Extensive simulations have been conducted to verify the accuracy and effectiveness of the proposed analytical derivations. The presented results verify that the security performance of the cooperative NOMA-based IoT network can be improved through an appropriate power control scheme and by generating AN at the BS and the strong IoT node. The simulation results further illustrate that the asymptotic SOP is close to the exact one.

Feature selection commonly refers to a process of using the candidate algorithm to detect the optimal feature sets during the preprocessing steps in machine learning and data mining. This procedure can optimize the dataset's features to be analyzed and maximize the classification performance based on the selected optimal feature combination. In this work, a hybridization model is developed and utilized to select optimal feature subset based on an innovative binary version of moth-flame optimizer and the K-Nearest Neighbor Classifier (KNN) for classification tasks. The proposed new technique, abbreviated as MFeature or ESAMFO, applies several strategies, including two types of transfer functions, ensemble strategy, simulated annealing (SA) disturbance mechanism, and crossover scheme to improve the equilibrium between the global exploration and local exploitation capabilities of the basic MFO. Each individual in the proposed algorithm is evaluated by the size of selected features and the error rate of the KNN classifier. The proposed model's efficacy is assessed on 30 benchmark datasets with different dimensions from the UCI repository and compared with other KNN based feature selection algorithms from the literature. The comprehensive results via various comparisons reveal the efficiency of the proposed technique in decreasing the classification error rate compared with other feature selection algorithms, ensuring the capability of ESAMFO in exploring the feature space and selecting the most informative features for classification purposes. For post publications that support this research, readers can refer to https://aliasgharheidari.com .

BACKGROUND: It is of great clinical significance to develop an accurate computer aided system to accurately diagnose the breast cancer. In this study, an enhanced machine learning framework is established to diagnose the breast cancer. The core of this framework is to adopt fruit fly optimization algorithm (FOA) enhanced by Levy flight (LF) strategy (LFOA) to optimize two key parameters of support vector machine (SVM) and build LFOA-based SVM (LFOA-SVM) for diagnosing the breast cancer. The high-level features abstracted from the volunteers are utilized to diagnose the breast cancer for the first time. RESULTS: In order to verify the effectiveness of the proposed method, 10-fold cross-validation method is used to make comparison among the proposed method, FOA-SVM (model based on original FOA), PSO-SVM (model based on original particle swarm optimization), GA-SVM (model based on genetic algorithm), random forest, back propagation neural network and SVM. The main novelty of LFOA-SVM lies in the combination of FOA with LF strategy that enhances the quality for FOA, thus improving the convergence rate of the FOA optimization process as well as the probability of escaping from local optimal solution. CONCLUSIONS: The experimental results demonstrate that the proposed LFOA-SVM method can beat other counterparts in terms of various performance metrics. It can very well distinguish malignant breast cancer from benign ones and assist the doctor with clinical diagnosis.