Clustering on high-dimensional and low sample size (HDLSS) data remains a critical, persistent challenge where extreme sparsity and noise confound cluster analysis. This creates a dilemma: spectral methods fail as distance metrics degrade, while deep clustering tends to over-fit scarce data. To break this dilemma, a Self-Enhanced Density Clustering (SEDC) framework that integrates the cluster structure discovery and embedding representation learning into an iterative enhancement process is proposed in this paper. Specifically, SEDC uses adaptive density-derived centroids to parameterize probabilistic soft labels, which in turn supervise a lightweight multilayer perceptron (MLP) to learn the low-dimensional embedding from data. The resulting embedding provides a refined metric space for further generating superior labels in the subsequent interaction process. This feedback forms a mutual reinforcement that progressively enhances the discrimination of embedding while rigorously mitigating over-fitting. Extensive experiments on 43 challenging HDLSS datasets demonstrate state-of-the-art performance, substantially outperforming popular clustering methods. This work delivers a principled and promising solution for robust data clustering in HDLSS situations.

Semi-supervised learning can leverage both labeled and unlabeled samples simultaneously to improve performance. However, existing methods often present the following issues: (1) The emphasis of learning is put on either the similarity structures or the regression losses of data, neglecting the interaction between them. (2) The similarity structures among boundary samples might be unreliable, which misleads label propagation and impairs the performance of models on out-of-sample data. (3) They often involve the inverses of high-order matrices, making them inefficient in computation. To overcome these issues, we propose a scalable semi-supervised learning framework with Discriminative Label Propagation and Correction (DLPC), which collaboratively exploits the regression losses and similarity structures of data. Particularly, each sample is projected onto the independent class labels associated with nonnegative adjustment vectors rather than the propagated labels, such that the distances between samples from different classes are naturally enlarged, making regression losses more effective for boundary samples. Benefiting from this, the regression losses can guide the propagation of labels in boundary areas. Thus, the label information is first propagated through dynamically optimized graph structures and then corrected by the regression losses, effectively improving the quality of labels and facilitating feature projection learning. Furthermore, an accelerated solution has been developed to reduce the computational costs of DLPC on sample scales, thereby making it scalable to relatively large-scale problems. Moreover, the proposed DLPC can not only be applied to single-view scenarios but also extended to multi-view tasks. Additionally, an optimization strategy with fast convergence has been presented for DLPC, and extensive experiments demonstrate the effectiveness and superiority of DLPC over state-of-the-art competitors.

Clustering complex-shaped clusters is still chal lenging for most existing clustering algorithms. Herein, the peak-padding clustering algorithm (PeakPad)-clustering by padding density peaks with the minimum padding cost-is proposed. PeakPad executes clustering on the density surface and views complex-shaped clusters as combinations of highly associated single-peak clusters. The minimum padding cost that fully considers the surrounding context of a density peak is proposed to reflect a density peak's center potential, enabling PeakPad to have robust center detection performance. Unlike mean-shift (MSC), which detects centers based on their attributes in a complex-shaped density surface embedded in the high-dimensional space of density and features, PeakPad detects centers in a standard-shaped surface embedded in the 2-D density-change (DC) density space (composed of density and DC feature). Such standardization allows PeakPad to have fast and robust cluster center detection performance on complex-shaped clusters based on the minimum padding cost. Besides, PeakPad can provide a reasonable evaluation of the association between single-peak clusters by using the minimum padding cost. As a result, PeakPad can fast capture complex-shaped clusters, achieve robust center detection performance, and be suitable for large datasets. Benchmark test results on both synthetic and real datasets demonstrate the effectiveness of PeakPad.

As partial samples are often absent in certain views, incomplete multi-view clustering has become a challenging task. To tackle data with missing views, current methods either utilize the data similarity relations to recover missing samples or primarily consider the available information of existing samples, typically facing some inherent limitations. Firstly, traditional solutions cannot fully explore the potential information contained in missing samples due to their omission strategy, leading to sub-optimal graphs. Moreover, most methods mainly focus on data recovery from the view level, ignoring the differences among available/missing samples in various views. To this end, we propose a collaborative Similarity Fusion and Consistency Recovery (SFCR) method, which resolves the incomplete multi-view clustering problem by learning a unified similarity graph and recovering missing samples with consistent structures. Specifically, to learn a reliable graph compatible across views, a novel view-to-sample fusion model is designed to adaptively coalesce the view-wise similarities among available samples, not only preserving the complementarity and consistency among views but also properly balancing different samples. Furthermore, the missing samples are effectively recovered under the guidance of the fused similarity graph, so as to maintain the consistent structure of recovered data across views. In this way, the similarity learning and the missing data recovery benefit from each other in a collaborative reinforcement manner. Meanwhile, SFCR can directly obtain the final clustering labels without additional post-processing. Extensive experiments demonstrate the effectiveness and superiority of SFCR.

As a foundational clustering paradigm, Density Peak Clustering (DPC) partitions samples into clusters based on their density peaks, garnering widespread attention. However, traditional DPC methods usually focus on high-density regions, neglecting representative peaks in relatively low-density areas, particularly in datasets with varying densities and multiple peaks. Moreover, existing DPC variants struggle to identify clusters correctly in high-dimensional spaces due to the indistinct distance differences among samples and sparse data distributions. Additionally, existing methods typically adopt a one-step label assignment strategy, making them prone to cascading errors when initial misassignments occur. To address these challenges, we propose an Enhanced Density Peak Clustering (EDPC) method, which creatively incorporates multilayer perceptron (MLP)-based dimensionality reduction and a hierarchical label assignment strategy to significantly improve clustering performance in high-dimensional scenarios. Specifically, we introduce an effective selection condition that combines average densities and density-related distances to generate potential cluster centers, ensuring that peaks across different density regions are considered simultaneously. Furthermore, an MLP, guided by pseudo-labels from sub-clusters, is designed to learn low-dimensional embeddings for high-dimensional data, preserving data locality while enhancing clusterability. Extensive experiments demonstrate the effectiveness and superiority of EDPC against state-of-the-art DPC methods.

Multi-Objective evolutionary algorithms (MOEAs) are popular for solving complex optimization problems with multiple objectives and they have gained much success in benchmark competitions as well as real-world applications. Most MOEAs are effective for specific types of problems, leading to a lack of generality. This paper presents a dynamic MOEA employing a learning strategy to adaptively allocate the appropriate optimizer for the given problem. Three key issues are considered by the designed learning strategy, including environmental information collection, information-reward conversion, and dynamic method selection. In the dynamic selection process, a set of candidate algorithms with various features is required, which improves the universality of our method. Extensive experimental comparisons have been done based on the benchmark functions WFG1–WFG9, as well as a real-world problem, which shows that the overall performance of the proposed method is superior to the state-of-the-art peer algorithms.

The concept of fuzzy domain adaptation (FDA) is focused on transferring a model trained in a source domain to a target domain, where intrinsic distribution discrepancies exist in non-stationary and non-deterministic environments. In this paper, a novel drift decoupling-based variational adaptation network (DD-VAN) is proposed for FDA, allowing for the learning of intra-domain evolutionary patterns and inter-domain uncertainties. The DD-VAN algorithm is implemented in three main steps: (1) an intra-domain evolutionary trend modeling module is first employed to capture unknown temporal variations through an autoencoder architecture with variational inference; (2) a prototype-assisted fuzzy clustering module is used to estimate the membership degree of the target data, characterizing the inherent uncertainty and imprecision present in real-world distributions; and (3) a membership-aware domain fuzzy matching module is utilized to learn the gradual transitions between category-related data pairs in the source and target domains by introducing uncertainties. Furthermore, it is theoretically demonstrated that the inferred posterior distributions of latent codes can be optimized to align with the corresponding prior distributions by minimizing the Kullback-Leibler divergence. Extensive experiments are conducted on cross-domain tasks involving both synthetic and realworld datasets, and the experimental results suggest that the DDVAN algorithm outperforms existing state-of-the-art methods.

Outdoor panoramic depth estimation faces significant challenges due to the wide field of view (FoV), complex scene structures, and severe distortion encountered in such environments. Traditional methods, which often use distortion convolution, fall short in capturing global distortion information and extracting rich contextual details from panoramic images. To overcome these limitations, this article introduces a novel dual-branch framework that synergistically merges the advantages of equirectangular projection (ERP) and tangent projection (TP). First, we design a unique dual-branch framework specifically tailored for panoramic depth estimation. In this framework, the convolutional neural networks branch processes ERP images to extract rich local information, enhancing the detail accuracy of depth estimation, while the vision transformers branch processes TPs to capture comprehensive global information, improving the smoothness of depth estimation. Then, we further enhance our method with a distortion-aware weight map module that adapts the influence of different image regions according to their distortion level, thus prioritizing features from areas with less distortion. In addition, we implement a dual attention fusion module to seamlessly integrate features from both branches at corresponding layers. Comprehensive experiments across various outdoor datasets reveal that our method significantly outperforms state-of-the-art techniques in terms of depth estimation accuracy, adeptly balancing the capture of both overarching scene depth and intricate details, potentially revolutionizing applications in industrial informatics, such as autonomous navigation and environmental mapping.

To address the challenges encountered by dictionary learning-based monitoring, this article presents a novel pattern discovery scheme for detection and isolation of incipient faults that involves structured sparse coding and sequential dictionary augmentations. Through learning a basic dictionary for normal pattern and augmenting the low-dimensional sparse dictionaries for analyzing different fault patterns, the process signals can be decomposed into fault-free and fault-related components. To guarantee the in-statistical-control status of the fault-free part and improve detection sensitivity, a$\ell _{2}$-penalty is imposed on the sum of coefficient vectors to ensure that the monitoring statistic related to the fault-free part will not exceed the control limit. In addition, two Frobenius norm penalties are imposed on the zero centered coefficient matrix and atom matrix to improve the robustness of signal decomposition. Instead of imposing$\ell _{1}$-sparsity constraint on the atoms, a hard sparsity constraint is used to correctly select fault-related feature variables, so that fault patterns can be better revealed. The informative dictionaries are then incorporated into the moving window-based monitoring strategy, yielding a fault detection and isolation scheme suitable for incipient faults. The superior performance of our proposed approach is validated by application studies involving a numerical example and two practical industrial processes.

This article is concerned with the distributed set-membership fusion estimation problem for a class of artificial neural networks (ANNs), where the dynamic event-triggered mechanism (ETM) is utilized to schedule the signal transmission from sensors to local estimators to save resource consumption and avoid data congestion. The main purpose of this article is to design a distributed set-membership fusion estimation algorithm that ensures the global estimation error resides in a zonotope at each time instant and, meanwhile, the radius of the zonotope is ultimately bounded. By means of the zonotope properties and the linear matrix inequality (LMI) technique, the zonotope restraining the prediction error is first calculated to improve the prediction accuracy and subsequently, the zonotope enclosing the local estimation error is derived to enhance the estimation performance. By taking into account the side-effect of the order reduction technique (utilized in designing the local estimation algorithm) of the zonotope, a sufficient condition is derived to guarantee the ultimate boundedness of the radius of the zonotope that encompasses the local estimation error. Furthermore, parameters of the local estimators are obtained via solutions to certain bilinear matrix inequalities. Moreover, the zonotope-based distributed fusion estimator is obtained through minimizing certain upper bound of the radius of the zonotope (that contains the global estimation error) according to the matrix-weighted fusion rule. Finally, the effectiveness of the proposed distributed fusion estimation method is illustrated via a numerical example.

Multi-view semi-supervised feature selection, aims to simultaneously exploit both labeled and unlabeled samples to select a subset of features from multiple feature representations, has become an important task. However, the performance of existing methods is susceptible to the quality of graph due to the following reasons: 1) The samples from different classes located near the boundary are quite close and fail to be classified accurately, causing the unclear neighbor structures. 2) The graph is directly derived from the original space, such that the low-quality features will undermine the true relation between samples. To address above issues, we propose a novel multi-view semi-supervised feature selection method (MVFS), which exploits the regression losses of samples to correct the label information inaccurately propagated via the unreliable neighbor structures on the boundary samples, so as to enhance the discrimination of prediction labels. Moreover, the data representation generated by graph convolutional networks (GCN), which integrates the features, neighbor structures and label information, is incorporated to adaptively update similarity graph to better capture the neighbor structures of samples. Benefiting from these, the discriminative prediction labels and a reliable similarity graph are learned to facilitate the final feature selection. An efficient solution is designed to iteratively optimize MVFS, and comprehensive experiments demonstrate the effectiveness of MVFS.

Locating lesions is the primary goal of colonoscopy examinations.3D perception techniques can enhance the accuracy of lesion localization by restoring 3D spatial information of the colon. However, existing methods focus on the local depth estimation of a single frame and neglect the precise global positioning of the colonoscope, thus failing to provide the accurate 3D location of lesions. The root causes of this shortfall is twofold: Firstly, existing methods treat colon depth and colonoscope pose estimation as independent tasks or design them as parallel sub-task branches. Secondly, the light source in the colon environment moves with the colonoscope, leading to brightness fluctuations among continuous frame images. To address these two issues, we propose ColVO, a novel deep learning-based Visual Odometry framework, which can continuously estimate colon depth and colonoscopic pose using two key components: a deep couple strategy for depth and pose estimation (DCDP) and a light consistent calibration mechanism (LCC). DCDP utilization of multimodal fusion and loss function constraints to couple depth and pose estimation modes ensure seamless alignment of geometric projections between consecutive frames. Meanwhile, LCC accounts for brightness variations by recalibrating the luminosity values of adjacent frames, enhancing ColVO's robustness. A comprehensive evaluation of ColVO on colon odometry benchmarks reveals its superiority over state-of-the-art methods in depth and pose estimation. We also demonstrate two valuable applications: immediate polyp localization and complete 3D reconstruction of the intestine. The code for ColVO is available at https://github.com/HNUicda/CoIVO.

To tackle the high-dimensional data with multiple representations, multi-view unsupervised feature selection has emerged as a significant learning paradigm. However, previous methods suffer from the following dilemmas: (i) They focus on selecting the features that preserve the similarity structure of data, whereas neglecting the discriminative information in the cluster structure; (ii) The orthogonal constraint is often imposed on the pseudo cluster labels, breaking the locality in the cluster label space; (iii) Learning the similarity or cluster structure from all samples is time-consuming. To this end, a Scalable Multi-view Unsupervised Feature Selection with structure learning and fusion (SMUFS) is proposed to jointly exploit the cluster structure and the similarity relations of data. Specifically, SMUFS introduces the sample-view weights to adaptively fuse the membership matrices that indicate cluster structures and serve as the pseudo cluster labels, such that a unified membership matrix across views can be effectively obtained to guide feature selection. Meanwhile, SMUFS performs graph learning from the membership matrix, preserving the locality of cluster labels and improving their discriminative capability. Further, an acceleration strategy has been developed to make SMUFS scalable for large-scale data. An iterative optimization is designed to solve the formulated objective function, and extensive experiments demonstrate the superiority of SMUFS.

Three-Dimensional (3D) dense captioning is an emerging vision-language bridging task that aims to generate multiple detailed and accurate descriptions for 3D scenes. It presents significant potential and challenges due to its closer representation of the real world compared to 2D visual captioning, as well as complexities in data collection and processing of 3D point cloud sources. Despite the popularity and success of existing methods, there is a lack of comprehensive surveys summarizing the advancements in this field, which hinders its progress. In this paper, we provide a comprehensive review of 3D dense captioning, covering task definition, architecture classification, dataset analysis, evaluation metrics, and in-depth prosperity discussions. Based on a synthesis of previous literature, we refine a standard pipeline that serves as a common paradigm for existing methods. We also introduce a clear taxonomy of existing models, summarize technologies involved in different modules, and conduct detailed experiment analysis. Instead of a chronological order introduction, we categorize the methods into different classes to facilitate exploration and analysis of the differences and connections among existing techniques. We also provide a reading guideline to assist readers with different backgrounds and purposes in reading efficiently. Furthermore, we propose a series of promising future directions for 3D dense captioning by identifying challenges and aligning them with the development of related tasks, offering valuable insights and inspiring future research in this field. Our aim is to provide a comprehensive understanding of 3D dense captioning, foster further investigations, and contribute to the development of novel applications in multimedia and related domains.

This article is concerned with domain generalization (DG), a practical yet challenging scenario in transfer learning where the target data are not available in advance. The key insight of DG is focused on learning a robust model that can generalize to the unseen domain by leveraging knowledge from the source domain. To this end, we propose a novel algorithm known as support-sample-assisted Adversarial Attacks (SSAA) for DG. In the SSAA algorithm, an attack–defense strategy is deployed to enhance the target model's generalizability and transferability. This strategy includes a nontargeted attack stage, during which attack samples are generated to form pseudotarget domains with near-realistic covariate shifts. Subsequently, in the model defense stage, a biclassifier structure is used to distinguish support samples from the generated attack samples. These support samples form a new decision boundary encompassing all unseen samples, prompting an extension of the existing decision boundary to meet these samples. Experimental results on cross-domain fault diagnosis tasks suggest that SSAA outperforms current state-of-the-art DG methods, indicating a promising avenue for further DG development.

Data augmentation (DA) has the potential to address the issue of imbalanced and insufficient datasets (I&ID) in pipeline fault diagnosis. However, the majority of existing DA methods for time series are inspired by computer vision techniques, ignoring the temporal dynamic properties and fine-grained fault features, which leads to limited performance of the augmentation. To tackle this problem, we introduce a novel DA approach called the subdomain-alignment adversarial self-attention network (SA-ASN), which takes into account both temporal association and semantic correlation. Our approach features a novel temporal association learning (TAL) mechanism, which transfers temporal information from the discriminator to the generator via a customized knowledge-sharing structure, improving the reliability of synthetic long-range associations. Additionally, we introduce a prototype-assisted subdomain alignment (PASA) strategy that forms a hierarchical structure in the synthetic dataset by incorporating local semantic correlation into the model training. With the support of TAL and PASA, our SA-ASN algorithm enhances the authenticity of temporal structure at the instance level and improves the discriminability of fault features at the category level. Our experimental results show that the SA-ASN algorithm provides a more diverse and accurate augmentation of pipeline data. The effectiveness of our SA-ASN algorithm encourages the use of data-driven diagnostic models in complex real-world oilfield pipeline networks.

As data sources become ever more numerous with increased feature dimensionality, feature selection for multiview data has become an important technique in machine learning. Semi-supervised multiview feature selection (SMFS) focuses on the problem of how to obtain a discriminative feature subset from heterogeneous feature spaces in the case of abundant unlabeled data with little labeled data. Most existing methods suffer from unreliable similarity graph structure across different views since they separate the graph construction from feature selection and use the fixed graphs that are susceptible to noisy features. Furthermore, they directly concatenate multiple feature projections for feature selection, neglecting the contribution diversity among projections. To alleviate these problems, we present an SMFS to simultaneously select informative features and learn a unified graph through the data fusion from aspects of feature projection and similarity graph. Specifically, SMFS adaptively weights different feature projections and flexibly fuses them to form a joint weighted projection, preserving the complementarity and consensus of the original views. Moreover, an implicit graph fusion is devised to dynamically learn a compatible graph across views according to the similarity structure in the learned projection subspace, where the undesirable effects of noisy features are largely alleviated. A convergent method is derived to iteratively optimize SMFS. Experiments on various datasets validate the effectiveness and superiority of SMFS over state-of-the-art methods.

This brief is concerned with the problem of kernel adaptive filtering for a complex network. First, a coupled kernel least mean square (KLMS) algorithm is developed for each node to uncover its nonlinear measurement function by using a series of input-output data. Subsequently, an upper bound is derived for the step-size of the coupled KLMS algorithm to guarantee the mean square convergence. It is shown that the upper bound is dependent on the coupling weights of the complex network. Especially, an optimal step size is obtained to achieve the fastest convergence speed and a suboptimal step size is presented for the purpose of practical implementations. Besides, a coupled kernel recursive least square (KRLS) algorithm is further proposed to improve the filtering performance. Finally, simulations are provided to verify the validity of the theoretical results.

This article is concerned with a new partial-neurons-based proportional-integral observer (PIO) design problem for a class of artificial neural networks (ANNs) subject to bounded disturbances. For the purpose of improving the reliability of the data transmission, the multiple description encoding mechanisms are exploited to encode the measurement data into two identically important descriptions, and the encoded data are then transmitted to the decoders via two individual communication channels susceptible to packet dropouts, where Bernoulli-distributed stochastic variables are utilized to characterize the random occurrence of the packet dropouts. An explicit relationship is discovered that quantifies the influences of the packet dropouts on the decoding accuracy, and a sufficient condition is provided to assess the boundedness of the estimation error dynamics. Furthermore, the desired PIO parameters are calculated by solving two optimization problems based on two metrics (i.e., the smallest ultimate bound and the fastest decay rate) characterizing the estimation performance. Finally, the applicability and advantage of the proposed PIO design strategy are verified by means of an illustrative example.

This paper proposes a differential evolution with clustering-based niching and adaptive mutation (CMDE) for global optimization. In the proposed algorithm, a clustering method is first employed to adaptively divide the population into niches according to the stage of evolution as well as the diversity of population. Based on the obtained niches, an adaptive mutation scheme is then devised such that encouraging high potential niches for exploitation while low potential niches for exploration, thus appropriately search the space. The performance of proposed algorithm has been evaluated on CEC'2015 test suit and compared with related methods. The results show that the proposed clustering-based niching and adaptive mutation schemes could be promising to enhance the DE for optimization.

Doctors perform limited one-way intestine endoscopy, in which advanced surgical robots with depth sensors, such as stereo and ToF endoscopes, can only provide sparse and incomplete depth information. However, dense, accurate and instant depth estimation during endoscopy is vital for doctors to judge the 3D location and shape of intestinal tissues, which affects the human-robot interaction between doctors and surgical robots, such as the operation on the subsequent moving of the probe. In this paper, we present a deep learning-based dense depth completion method for intestine endoscopy. We utilize the scattered depth information from depth sensors to make up for the deficiency of features in the intestine and design a multi-scale confidence prediction network to extract dense geometric depth features. Then, we introduce the structure awareness module based on the self-attention mechanism in the depth completion network to enhance the geometry and texture features of the intestine. We also present a virtual multi-modal RGBD intestine dataset and conduct comprehensive experiments on a total of three intestine datasets. The experimental results clearly demonstrate that our method achieves better results in all metrics in all intestinal environments compared to state-of-the-art methods.

In large-scale natural disasters and special criminal cases, surface features of bodies, such as faces and fingerprints, are easily destroyed. Teeth possess strong high-temperature resistance, corrosion resistance, and high hardness, which can compensate for the shortcomings of the aforementioned situations. This paper proposes an identification method based on the aggregated features of multi-scale dental impression images. Firstly, a method exploiting the adaptive object detection method based on YOLOv8 is proposed to segment toothprints. Next, a novel geometric feature named calibrated offset distance is extracted, combined with the SIFT feature method, to extract multi-scale and multi-dimensional features from the global toothprint, local toothprints, and single-tooth prints. Finally, all features are aggregated to enhance the descriptive ability and robustness. Experimental results indicate that the method proposed in this paper demonstrates good identification performance.

The novel coronavirus pneumonia (COVID-19) has created great demands for medical resources. Determining these demands timely and accurately is critically important for the prevention and control of the pandemic. However, even if the infection rate has been estimated, the demands of many medical materials are still difficult to estimate due to their complex relationships with the infection rate and insufficient historical data. To alleviate the difficulties, we propose a co-evolutionary transfer learning (CETL) method for predicting the demands of a set of medical materials, which is important in COVID-19 prevention and control. CETL reuses material demand knowledge not only from other epidemics, such as severe acute respiratory syndrome (SARS) and bird flu but also from natural and manmade disasters. The knowledge or data of these related tasks can also be relatively few and imbalanced. In CETL, each prediction task is implemented by a fuzzy deep contractive autoencoder (CAE), and all prediction networks are cooperatively evolved, simultaneously using intrapopulation evolution to learn task-specific knowledge in each domain and using interpopulation evolution to learn common knowledge shared across the domains. Experimental results show that CETL achieves high prediction accuracies compared to selected state-of-the-art transfer learning and multitask learning models on datasets during two stages of COVID-19 spreading in China.

In this article, a delay-range-dependent approach is put forward to tackle the state estimation problem for delayed impulsive neural networks. A new type of nonlinear function, which is more general than the normal sigmoid function and functions constrained by the Lipschitz condition, is adopted as the neuron activation function. To effectively alleviate data collisions and save energy, the round-robin protocol is utilized to mitigate the occurrence of unnecessary network congestion in communication channels from sensors to the estimator. With the aid of the Lyapunov stability theory, a state observer is constructed such that the estimation error dynamics are asymptotically stable. The observer existence is ensured by resorting to a set of delay-range-dependent criteria which is dependent on both the impulsive time instant and a coefficient matrix. In addition, the synthesis of the observer is discussed by using linear matrix inequalities. Simulations are provided to illustrate the reasonability of our delay-range-dependent estimation approach.

Properly configuring mutation strategies and their associated parameters in DE is inherently a difficult issue. In this paper, an adaptive multi-subpopulation based differential evolution has been proposed and employed for global optimization. In the proposed method, the entire population is firstly adaptively divided at each generation according to a devised population division strategy, which try to partition the population into multiple subpopulations according to the potential of individuals. Then, a suitable mutation strategy along with an appropriate parameter control scheme is introduced and assigned to each subpopulation for evolution, with the purpose of delivering a balanced evolution. The performance of proposed algorithm has been evaluated on CEC'2015 benchmark functions and compared with related methods. The results show that our method can outperform related methods to be compared.

This paper proposes an adaptive differential evolution algorithm with mutation strategy pools for global optimization. In the proposed method, the mutation strategy pool mechanism is devised to supply appropriate mutation strategy for different individuals in the population. Further, a mutation strategy, called DE/current-to-wb/l, has also been designed and employed in the mutation strategy pool. The performance of the proposed algorithm has been evaluated on CEC'2014 benchmark functions and compared with related methods. Experimental results show that the proposed algorithm has a good performance and outperforms related algorithms.

Clustering, as an important part of data mining, is inherently a challenging problem. This article proposes a differential evolution algorithm with adaptive niching and k -means operation (denoted as DE_ANS_AKO) for partitional data clustering. Within the proposed algorithm, an adaptive niching scheme, which can dynamically adjust the size of each niche in the population, is devised and integrated to prevent premature convergence of evolutionary search, thus appropriately searching the space to identify the optimal or near-optimal solution. Furthermore, to improve the search efficiency, an adaptive k -means operation has been designed and employed at the niche level of population. The performance of the proposed algorithm has been evaluated on synthetic as well as real datasets and compared with related methods. The experimental results reveal that the proposed algorithm is able to reliably and efficiently deliver high quality clustering solutions and generally outperforms related methods implemented for comparisons.

This article addresses the simultaneous state and unknown input estimation problem for a class of discrete time-varying complex networks (CNs) under redundant channels and dynamic event-triggered mechanisms (ETMs). The redundant channels, modeled by an array of mutually independent Bernoulli distributed stochastic variables, are exploited to enhance transmission reliability. For energy-saving purposes, a dynamic event-triggered transmission scheme is enforced to ensure that every sensor node sends its measurement to the corresponding estimator only when a certain condition holds. The primary objective of the investigation carried out is to construct a recursive estimator for both the state and the unknown input such that certain upper bounds on the estimation error covariances are first guaranteed and then minimized at each time instant in the presence of dynamic event-triggered strategies and redundant channels. By solving two series of recursive difference equations, the desired estimator gains are computed. Finally, an illustrative example is presented to show the usefulness of the developed estimator design method.

As data sources become ever more numerous, classification for multi-view data represented by heterogeneous features has been involved in many data mining applications. Most existing methods either directly concatenate all views or separately tackle each view, neglecting the correlation and diversity among views. Moreover, they often encounter an extra hyper-parameter that needs to be manually tuned, degenerating the applicability of models. In this paper, we present a robust supervised learning framework for multi-view classification, seeking a better representation and fusion of multiple views. Specifically, our framework discriminates different views with adaptively optimized view-wise weight factors and coalesces them to learn a joint projection subspace compatible across multiple views in an adaptive-weighting manner, thereby avoiding the intractable hyper-parameter. Meanwhile, the consensus and complementary information of original views can be naturally integrated into the learned subspace, in turn enhancing the discrimination of the subspace for subsequent classification. An efficient convergent algorithm is developed to iteratively optimize the formulated framework. Experiments on real datasets demonstrate the effectiveness and superiority of the proposed method.

This article is concerned with the problem of finite-horizon H∞state estimation for time-varying coupled stochastic networks through the round-robin scheduling protocol. The inner coupling strengths of the considered coupled networks are governed by a random sequence with known expectations and variances. For the sake of mitigating the occurrence probability of the network-induced phenomena, the communication network is equipped with the round-robin protocol that schedules the signal transmissions of the sensors' measurement outputs. By using some dedicated approximation techniques, an uncertain auxiliary system with stochastic parameters is established where the multiplicative noises enter the coefficient matrix of the augmented disturbances. With the established auxiliary system, the desired finite-horizon H∞state estimator is acquired by solving coupled backward Riccati equations, and the corresponding recursive estimator design algorithm is presented that is suitable for online application. The effectiveness of the proposed estimator design method is validated via a numerical example.

Recent studies in multiobjective particle swarm optimization (PSO) have the tendency to employ Pareto-based technique, which has a certain effect. However, they will encounter difficulties in their scalability upon many-objective optimization problems (MaOPs) due to the poor discriminability of Pareto optimality, which will affect the selection of leaders, thereby deteriorating the effectiveness of the algorithm. This paper presents a new scheme of discriminating the solutions in objective space. Based on the properties of Pareto optimality, we propose the dominant difference of a solution, which can demonstrate its dominance in every dimension. By investigating the norm of dominant difference among the entire population, the discriminability between the candidates that are difficult to obtain in the objective space is obtained indirectly. By integrating it into PSO, we gained a novel algorithm named many-objective PSO based on the norm of dominant difference (MOPSO/DD) for dealing with MaOPs. Moreover, we design a Lp-norm-based density estimator which makes MOPSO/DD not only have good convergence and diversity but also have lower complexity. Experiments on benchmark problems demonstrate that our proposal is competitive with respect to the state-of-the-art MOPSOs and multiobjective evolutionary algorithms.

Our previous study has constructed a deep learning model for predicting gastrointestinal infection morbidity based on environmental pollutant indicators in some regions in central China. This article aims to adapt the prediction model for three purposes: 1) predicting the morbidity of a different disease in the same region; 2) predicting the morbidity of the same disease in a different region; and 3) predicting the morbidity of a different disease in a different region. We propose a tridirectional transfer learning approach, which achieves the abovementioned three purposes by: 1) developing a combined univariate regression and multivariate Gaussian model for establishing the relationship between the morbidity of the target disease and that of the source disease together with the high-level pollutant features in the current source region; 2) using mapping-based deep transfer learning to extend the current model to predict the morbidity of the source disease in both source and target regions; and 3) applying the pattern of the combined model in the source region to the extended model to derive a new combined model for predicting the morbidity of the target disease in the target region. We select gastric cancer as the target disease and use the proposed transfer learning approach to predict its morbidity in the source region and three target regions. The results show that, given only a limited number of labeled samples, our approach achieves an average prediction accuracy of over 80% in the source region and up to 78% in the target regions, which can contribute considerably to improving medical preparedness and response.

Water Wave Optimization (WWO), as a nature-inspired optimization algorithm, has received much attention. In this paper, we tend to improve the algorithm by proposing an adaptive WWO with enhanced wave interaction (AWWOEI). In the proposed method, two operators (namely, Gaussian-based propagation and refraction learning) with an adaptive mechanism are introduced to enhance the wave interaction in the algorithm. The first operator, Gaussian-based propagation operation, is designed to strengthen the exploration ability of the algorithm by encouraging each individual to learn from different exemplars. While, the second operator, refraction learning, aims to improve the exploitation capability of WWO. Further, rather than using a fixed breaking coefficient, an adaptive mechanism has been employed to dynamically adjust its values during evolution. Experiments have been carried out to evaluate the performance of the proposed method and compare it with related methods. The results have demonstrated the superiority of the proposed method.

Traditional building identification methods are difficult for extracting the specific information of various buildings. In this paper, A transfer learning method with multi-feature calibration is proposed for building identification. Our model is based on the pre-training and fine-tuning framework of transfer learning. First, a CNN-based feature extractor, pre-trained by ImageNet, is adopted to extract features, then flatten the feature maps and feed it to a fully-connected network for image classification. This basic transfer learning model can correctly identify 81.2% of test samples. Further, a multi-feature calibration method is proposed. By defining the features of multi-functional buildings artificially, the feature vectors via the extractor are more representative and it can be efficiently applied on some small-sample data sets. We use a self-made building data set to test our methods. The experimental results show that the recognition accurate rate of the model with multi-feature calibration attains to 91.9%.

Most existing machine vision‐based location methods mainly focus on the spatial positioning schemes using one or two cameras along with non‐vision sensors. To achieve an accurate location, both schemes require processing a large amount of data. In this study, the authors propose a novel method, which requires much less amount of data to be processed for measuring target distance using monocular vision. Based on the geometric model of camera imaging, the parameters of the camera (such as camera's focal length and equivalent focal length.), as well as the principle of analogue signal being transformed into a digital signal, the authors derive the relationship among the target distance, field of view, equivalent focal length and camera resolution. Experimental results show that the proposed method can effectively and accurately achieve the target distance measurement.

This paper proposes a novel event-triggered (ET) adaptive neural control scheme for a class of discrete-time nonlinear systems in a strict-feedback form. In the proposed scheme, the ideal control input is derived in a recursive design process, which relies on system states only and is unrelated to virtual control laws. In this case, the high-order neural networks (NNs) are used to approximate the ideal control input (but not the virtual control laws), and then the corresponding adaptive neural controller is developed under the ET mechanism. A modified NN weight updating law, nonperiodically tuned at triggering instants, is designed to guarantee the uniformly ultimate boundedness (UUB) of NN weight estimates for all sampling times. In virtue of the bounded NN weight estimates and a dead-zone operator, the ET condition together with an adaptive ET threshold coefficient is constructed to guarantee the UUB of the closed-loop networked control system through the Lyapunov stability theory, thereby largely easing the network communication load. The proposed ET condition is easy to implement because of the avoidance of: 1) the use of the intermediate ET conditions in the backstepping procedure; 2) the computation of virtual control laws; and 3) the redundant triggering of events when the system states converge to a desired region. The validity of the presented scheme is demonstrated by simulation results.

This paper focuses on the observer-based output-feedback control (OBOFC) problem for a class of discrete-time strict-feedback nonlinear systems (DTSFNSs) with both multiplicative process noises and additive measurement noises. A state observer is first designed to estimate immeasurable system states, and then a novel observer-based backstepping control framework is proposed for DTSFNSs with known model information. To be specific, virtual control laws and the actual control law are derived using a variable substitution method that gets rid of the repeated accumulation of measurement noises in the recursive process. Furthermore, for technical derivation, the multiplicative noise is successively bounded by state estimation errors and controlled errors. Stability conditions are obtained to guarantee the exponential mean-square boundedness of the closed-loop system. Moreover, the nonlinear modeling uncertainties are taken into account to better reflect engineering practices. In virtue of the universal approximation property of fuzzy-logic systems, a fuzzy observer and the corresponding fuzzy output-feedback controller are simultaneously constructed to derive the stability criteria by using novel weight updated laws. Simulation studies are performed to test the validity of the proposed OBOFC scheme.

The use of unmanned aerial vehicles (UAVs) for target searching in complex environments has increased considerably in recent years. The numerous studies on UAV search methods have been reported, but few have been conducted on collaborative human-UAV search which is common in many applications. In this paper, we present a problem of collaborative human-UAV search for escaped criminals, the aim of which is to minimize the expected time of capture rather than detection. We show that our problem is much more complex than the problem of pure UAV search. The difficulty of our problem is further increased by the fact that criminals will attempt to avoid detection and capture. To solve the problem, we propose a hybrid evolutionary algorithm (EA) that uses three evolutionary operators, namely, comprehensive learning, variable mutation, and local search, to efficiently explore the solution space. The experimental results demonstrate that the proposed method outperforms some well-known EAs and other popular UAV search methods on test instances. An application of our method to a real-world operation took 311 min to capture a criminal who had escaped for over three days, validating its practicability and performance advantage. This paper provides a good basis for promoting the application of EAs to a wider class of man-machine collaboration scheduling problems.

This article investigates the mixed H2/H∞state estimation problem for a class of discrete-time switched complex networks with random coupling strengths through redundant communication channels. A sequence of random variables satisfying certain probability distributions is employed to describe the stochasticity of the coupling strengths. A redundant-channel-based data transmission mechanism is adopted to enhance the reliability of the transmission channel from the sensor to the estimator. The purpose of the addressed problem is to design a state estimator for each node, such that the error dynamics achieves both the stochastic stability (with probability 1) and the prespecified mixed H2/H∞performance requirement. By using the switched system theory, an extensive stochastic analysis is carried out to derive the sufficient conditions ensuring the stochastic stability as well as the mixed H2/H∞performance index. The desired state estimator is also parameterized by resorting to the solutions to certain convex optimization problems. A numerical example is provided to illustrate the validity of the proposed estimation scheme.

This article deals with the recursive filtering issue for a class of nonlinear complex networks (CNs) with switching topologies, random sensor failures and dynamic event-triggered mechanisms. A Markov chain is utilized to characterize the switching behavior of the network topology. The phenomenon of sensor failures occurs in a random way governed by a set of stochastic variables obeying certain probability distributions. In order to save communication cost, a dynamic event-triggered transmission protocol is introduced into the transmission channel from the sensors to the recursive filters. The objective of the addressed problem is to design a set of dynamic event-triggered filters for the underlying CN with a certain guaranteed upper bound (on the filtering error covariance) that is then locally minimized. By employing the induction method, an upper bound is first obtained on the filtering error covariance and subsequently minimized by properly designing the filter parameters. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed filtering scheme.

This paper investigates the event-triggered (ET) tracking control problem for a class of discrete-time strict-feedback nonlinear systems subject to both stochastic noises and limited controller-to-actuator communication capacities. The ET mechanism with fixed triggering threshold is designed to decide whether the current control signal should be transmitted to the actuator. A systematic framework is developed to construct a novel adaptive neural controller by directly applying the backstepping procedure to the underlying system. The proposed framework overcomes the noncausality problem, avoids the possible controller-related singularity problem, and gets rid of the neural approximation of the virtual control laws. Under the ET mechanism, the corresponding ET-based actuator is put forward by introducing an ET threshold compensation operator. Such a compensation operator (with an adjustable design parameter) is subtly designed based on a hyperbolic tangent function and a sign function. The threshold compensation error is analytically characterized in terms of a time-varying parameter, and the error bound is shown to be relatively small that is dependent on the adjustable design parameter. Compared with the traditional ET-based actuator without the compensation operator, the proposed ET-based actuator exhibits several distinguished features including: 1) improvement of the tracking accuracy (especially at the triggering instants); 2) further mitigation of the communication load; and 3) enlargement of the allowable range of the ET threshold. These features are illustrated by numerical and practical examples.

Generative Adversarial Network (GAN) has been employed for single image super-resolution (SISR). However, unregularized GAN is difficult for training. This is due gradient descent based GAN optimization is not easy to convergence, thus limiting its performance for image super-resolution. In this paper, a relativistic super-resolution GAN with a simplified gradient penalty (RSRGAN-GP) is proposed for single image super-resolution. In the proposed method, a compact residual network optimized by removing Batch-Normalization layers is employed as the generator to estimate photo-realistic images of 4× upscaling. Further, we introduce a residual network, which also has no Batch-Normalization layers as the conditional discriminator and adopt a simplified gradient regularization to penalize it for stabilizing the super-resolution GAN training, thus guaranteeing high-quality image reconstruction. Additionally, the super-resolution GAN is enhanced with a relativistic discriminator, which produces sharp and rich-detail images at no extra computational cost. The results on benchmark datasets show that our proposed method can effectively improve the visual quality of super-resolved images and achieves competitive performance compared with related works.

Due to the uncertainty of diseases, traditional approaches of drug procurement planning in hospitals often cause drug overstocking or understocking, which can have strong negative effects on healthcare services. This paper proposes a big-data driven approach, which uses a deep neural network to predict morbidities of acute gastrointestinal infections based on a huge amount of environmental data, and then constructs an optimization problem of drug procurement planning for maximizing the expected therapeutic effect on the predicted cases. The problem is solved by an efficient heuristic optimization algorithm. Computational experiments demonstrate the performance advantages of both the deep learning model and the heuristic algorithm over existing ones, and two real case studies in Central China show that the average prediction error of our approach is only 8% and the estimated recovery rate reaches 99%, much better than the currently used method. Our approach can also be extended for many other medical resource planning problems.

Teeth are difficult to be destroyed due to their corrosion resistance, high melting point and hardness. Dental biometrics can therefore provide assistance in human forensic identification, especially to the unknown corpses. One of the key issue in dental based human identification is the segmentation of Dental X‐ray images. In this paper, a novel segmentation algorithm has been proposed for this purpose. The proposed algorithm is based on full threshold segmentation. We first obtain the outline image set Iwhole n and crown image set Icrown m of the complete target tooth. Morphological open operation is then applied to the difference images of Iwhole n and Icrown m . Subsequently, the most complete target tooth image and its corresponding crown image are selected. Getting independent target tooth image I contour and its crown image I crown from these two images. Median filtering is applied to the synthetic image of I contour and I crown , and the resulted image will be used as the Mask for GrabCut to obtain the target tooth image. Experimental results show our proposed algorithm can effectively overcome the problems of uneven grayscale distribution and adhesion of adjacent crowns in dental X‐ray images. It can also achieve a high segmentation accuracy and outperform related methods to be compared.

Passenger profiling plays a vital part of commercial aviation security, but classical methods become very inefficient in handling the rapidly increasing amounts of electronic records. This paper proposes a deep learning approach to passenger profiling. The center of our approach is a Pythagorean fuzzy deep Boltzmann machine (PFDBM), whose parameters are expressed by Pythagorean fuzzy numbers such that each neuron can learn how a feature affects the production of the correct output from both the positive and negative sides. We propose a hybrid algorithm combining a gradient-based method and an evolutionary algorithm for training the PFDBM. Based on the novel learning model, we develop a deep neural network (DNN) for classifying normal passengers and potential attackers, and further develop an integrated DNN for identifying group attackers whose individual features are insufficient to reveal the abnormality. Experiments on data sets from Air China show that our approach provides much higher learning ability and classification accuracy than existing profilers. It is expected that the fuzzy deep learning approach can be adapted for a variety of complex pattern analysis tasks.

Automatic data clustering, whose goal is to recover the proper number of clusters as well as appropriate partitioning of data sets, is a fundamental yet challenging problem in unsupervised learning. In this paper, adaptive multisubpopulation competition (AMC) and multiniche crowding are proposed and incorporated into a memetic algorithm to tackle the problem. The AMC mechanism is developed to ensure a diverse search over solution subspaces corresponding to different numbers of clusters while allowing more promising subspaces to be more intensively searched. In this mechanism, the amount of individuals to be migrated between subpopulations is adaptively controlled according to the performance of subpopulations as well as the diversity of cluster numbers in population. Further, the migration is restricted to occur between subpopulations with relatively similar performances. Additionally, subpopulations with different performances are devised to search their corresponding subspaces with different exploration powers. The adaptive multiniche crowding scheme is designed to promote a diverse search of the subspace while allowing an efficient convergence of the corresponding subpopulation. This is achieved by dynamically adjusting parameter values of a multiniche crowding method to form and maintain diverged niches of high fitness within the subpopulation. The performance of proposed algorithm has been demonstrated through a series of experiments on both artificial and real data, and compared with existing methods. The results reveal that our proposed algorithm can achieve superior clustering performance and outperform related methods.

Storing biometric templates and/or encryption keys, as adopted in traditional biometrics-based authentication methods, has raised a matter of serious concern. To address such a concern, biometric key generation, which derives encryption keys directly from statistical features of biometric data, has emerged to be a promising approach. Existing methods of this approach, however, are generally unable to appropriately model user variations, making them difficult to produce consistent and discriminative keys of high entropy for authentication purposes. This paper develops a semisupervised clustering scheme, which is optimized through a niching memetic algorithm, to effectively and simultaneously model both intra- and interuser variations. The developed scheme is employed to model the user variations on both single features and feature subsets with the purpose of recovering a large number of consistent and discriminative feature elements for key generation. Moreover, the scheme is designed to output a large number of clusters, thus further assisting in producing long while consistent and discriminative keys. Based on this scheme, a biometric key generation method is finally proposed. The performance of the proposed method has been evaluated on the biometric modality of handwritten signatures and compared with existing methods. The results show that our method can deliver consistent and discriminative keys of high entropy, outperforming-related methods.

Clustering is deemed one of the most difficult and challenging problems in machine learning. In this paper, we propose a multilocal search and adaptive niching-based genetic algorithm with a consensus criterion for automatic data clustering. The proposed algorithm employs three local searches of different features in a sophisticated manner to efficiently exploit the decision space. Furthermore, we develop an adaptive niching method, which can dynamically adjust its parameter value depending on the problem instance as well as the search progress, and incorporate it into the proposed algorithm. The adaptation strategy is based on a newly devised population diversity index, which can be used to promote both genetic diversity and fitness. Consequently, diverged niches of high fitness can be formed and maintained in the population, making the approach well-suited to effective exploration of the complex decision space of clustering problems. The resulting algorithm has been used to optimize a consensus clustering criterion, which is suggested with the purpose of achieving reliable solutions. To evaluate the proposed algorithm, we have conducted a series of experiments on both synthetic and real data and compared it with other reported methods. The results show that our proposed algorithm can achieve superior performance, outperforming related methods.

Purpose Biometric authentication, which requires storage of biometric templates and/or encryption keys, raises a matter of serious concern, since the compromise of templates or keys necessarily compromises the information secured by those keys. To address such concerns, efforts based on dynamic key generation directly from the biometrics have recently emerged. However, previous methods often have quite unacceptable authentication performance and/or small key spaces and therefore are not viable in practice. The purpose of this paper is to propose a novel method which can reliably generate long keys while requires storage of neither biometric templates nor encryption keys. Design/methodology/approach This proposition is achieved by devising the use of fingerprint orientation fields for key generation. Additionally, the keys produced are not permanently linked to the orientation fields, hence, allowing them to be replaced in the event of key compromise. Findings The evaluation demonstrates that the proposed method for dynamic key generation can offer both good reliability and security in practice, and outperforms other related methods. Originality/value In this paper, the authors propose a novel method which can reliably generate long keys while requires storage of neither biometric templates nor encryption keys. This is achieved by devising the use of fingerprint orientation fields for key generation. Additionally, the keys produced are not permanently linked to the orientation fields, hence, allowing them to be replaced in the event of key compromise.

In this paper, we develop a fingerprint matching method which operates by first introducing an integrated measure, which combines two different matching criteria based on heterogeneous features. We then devise a genetically guided algorithm to optimise the integrated measure for simultaneous fingerprint alignment and verification. The proposed method is evaluated through experiments conducted on two public domain collections of fingerprint images and compared with related work, with very encouraging results.

Biometric authentication is increasingly gaining popularity in a wide range of applications. However, the storage of the biometric templates and/or encryption keys that are necessary for such applications is a matter of serious concern, as the compromise of templates or keys necessarily compromises the information secured by those keys. In this paper, we propose a novel method, which requires storage of neither biometric templates nor encryption keys, by directly generating the keys from statistical features of biometric data. An outline of the process is as follows: given biometric samples, a set of statistical features is first extracted from each sample. On each feature subset or single feature, we model the intra and interuser variation by clustering the data into natural clusters using a fuzzy genetic clustering algorithm. Based on the modelling results, we subsequently quantify the consistency of each feature subset or single feature for each user. By selecting the most consistent feature subsets and/or single features for each user individually, we generate the key reliably without compromising its relative security. The proposed method is evaluated on handwritten signature data and compared with related methods, and the results are very promising.

Clustering is inherently a difficult task, and is made even more difficult when the selection of relevant features is also an issue. In this paper we propose an approach for simultaneous clustering and feature selection using a niching memetic algorithm. Our approach (which we call NMA_CFS) makes feature selection an integral part of the global clustering search procedure and attempts to overcome the problem of identifying less promising locally optimal solutions in both clustering and feature selection, without making any a priori assumption about the number of clusters. Within the NMA_CFS procedure, a variable composite representation is devised to encode both feature selection and cluster centers with different numbers of clusters. Further, local search operations are introduced to refine feature selection and cluster centers encoded in the chromosomes. Finally, a niching method is integrated to preserve the population diversity and prevent premature convergence. In an experimental evaluation we demonstrate the effectiveness of the proposed approach and compare it with other related approaches, using both synthetic and real data.

Minutiae point pattern matching is the most common approach for fingerprint verification. Although many minutiae point pattern matching algorithms have been proposed, reliable automatic fingerprint verification remains as a challenging problem, both with respect to recovering the optimal alignment and the construction of an adequate matching function. In this paper, we develop a memetic fingerprint matching algorithm (MFMA) which aims to identify the optimal or near optimal global matching between two minutiae sets. Within the MFMA, we first introduce an efficient matching operation to produce an initial population of local alignment configurations by examining local features of minutiae. Then, we devise a hybrid evolutionary procedure by combining the use of the global search functionality of a genetic algorithm with a local improvement operator to search for the optimal or near optimal global alignment. Finally, we define a reliable matching function for fitness computation. The proposed algorithm was evaluated by means of a series of experiments conducted on the FVC2002 database and compared with previous work. Experimental results confirm that the MFMA is an effective and practical matching algorithm for fingerprint verification. The algorithm is faster and more accurate than a traditional genetic-algorithm-based method. It is also more accurate than a number of other methods implemented for comparison, though our method generally requires more computational time in performing fingerprint matching.

Clustering is inherently a difficult problem, both with respect to the construction of adequate objective functions as well as to the optimization of the objective functions. In this paper, we suggest an objective function called the Weighted Sum Validity Function (WSVF), which is a weighted sum of the several normalized cluster validity functions. Further, we propose a Hybrid Niching Genetic Algorithm (HNGA), which can be used for the optimization of the WSVF to automatically evolve the proper number of clusters as well as appropriate partitioning of the data set. Within the HNGA, a niching method is developed to preserve both the diversity of the population with respect to the number of clusters encoded in the individuals and the diversity of the subpopulation with the same number of clusters during the search. In addition, we hybridize the niching method with the k-means algorithm. In the experiments, we show the effectiveness of both the HNGA and the WSVF. In comparison with other related genetic clustering algorithms, the HNGA can consistently and efficiently converge to the best known optimum corresponding to the given data in concurrence with the convergence result. The WSVF is found generally able to improve the confidence of clustering solutions and achieve more accurate and robust results.

In this paper, we propose a novel local search heuristic and then hybridize it with a genetic algorithm for k-medoid clustering of large data sets, which is an NP-hard optimization problem. The local search heuristic selects k-medoids from the data set and tries to efficiently minimize the total dissimilarity within each cluster. In order to deal with the local optimality, the local search heuristic is hybridized with a genetic algorithm and then the Hybrid K-medoid Algorithm (HKA) is proposed. Our experiments show that, compared with previous genetic algorithm based k-medoid clustering approaches - GCA and RAR/sub w/GA, HKA can provide better clustering solutions and do so more efficiently. Experiments use two gene expression data sets, which may involve large noise components.