Max-min Distance Research Articles

An adaptive evolutionary search-based method for efficiently tackling the set-union knapsack problem

This paper tackles the NP-hard set-union knapsack problem using a novel adaptive evolutionary algorithm. Such a problem has applications in database query optimization, network design, and project portfolio management. The method starts by creating an archive set with elite and diversified solutions, employing a customized procedure based on item scores and Hamming max-min distance for diversity. Various operators are added to enhance solution quality in the elite set and refine the search process. To counteract premature convergence, combination and subset reference update methods are used. The fusion strategy serves as an exploration mechanism, generating new solutions and preventing premature convergence. Finally, the effectiveness and performance of the proposed method is evaluated on a set of benchmark instances taken from existing literature and by conducting a thorough statistical analysis of all achieved results. All obtained results are systematically compared against those achieved by state-of-the-art methods, indicating a substantial improvement of 50% for the large-sized instances, outperforming those published in the literature.

Study on cluster centers optimization of max-min distance k-means clustering algorithm based on inter-cluster separation measure

Clustering is an essential unsupervised technique when category information is not available. Although K-means and Max-min distance K-means clustering algorithms are widely used, they have some disadvantages such as dependence on the initial centers, sensitivity to outliers caused by using only distance as the clustering criterion. To overcome the problems, this paper proposes SMM-K-means algorithm which overcomes the dependence on the initial cluster centers and the initial number of clusters and the sensitivity to the outliers. First, the initial value K of the optimal cluster number is determined by the elbow method, and K-means is used for initial clustering. A new inter-cluster separation measure is then constructed based on the idea of q-nearest neighbors, which is constructed by comprehensive considering the separation between clusters and the distribution compactness of clusters themselves. Finally, the two sample points with highest degree of separation are brought into Max-min distance K-means algorithm as new initial centers for clustering. The definite determining method of cluster centers eliminates the complicated iterative calculation, and the construction of inter-cluster separation measure overcomes the sensitivity of clustering results to noise points and isolated points, and has good applicability and generalization. In addition, this algorithm is not limited by the shape and size of the clusters and has better flexibility. The experimental results show that the SMM-K-means algorithm has higher CH values, resulting in a better clustering effect and stability.

PaVa: A novel path-based valley-seeking clustering algorithm

Clustering methods are being applied to a wider range of scenarios involving more complex datasets, where the shapes of clusters tend to be arbitrary. In this paper, we propose a novel Path-based Valley-seeking clustering algorithm for arbitrarily shaped clusters. This work aims to seek the valleys among clusters and then individually extract clusters. Three vital techniques are used in this algorithm. First, path distance (minmax distance) is employed to transform the irregular boundaries among clusters, that is density valleys, into perfect spherical shells. Second, a suitable density measurement, k-distance, is employed to make adjustment on Minimum Spanning Tree, by which a robust minmax distance is calculated. Third, we seek the transformed density valleys by determining their centers and radii. Based on the vital techniques, the main contributions of the proposed algorithm can be summarized as follows. First, the clusters are wrapped in spherical shells after the distance transformation, making the extraction process efficient even with clusters of arbitrary shape. Second, adjusted Minimum Spanning Tree enhances the robustness of minmax distance under different kinds of noise. Last, the number of clusters does not need to be inputted or decided manually due to the individual extraction process. After applying the proposed algorithm to several commonly used synthetic datasets, the results indicate that the Path-based Valley-seeking algorithm is accurate and efficient. The algorithm is based on the dissimilarity of objects, so it can be applied to a wide range of fields. Its performance on real-world datasets illustrates its versatility.

No-depot minmax mTSP 문제를 위한 분할 및 가변 이웃 탐색 기반 휴리스틱

In this study, we present the SlGi heuristic to address the no-depot minmax mTSP, comprising both construction and improvement stages. For each stage, we introduce a k-slice method algorithm and a SAG-insertion algorithm. Comparative analysis against clustering algorithms reveals the superior performance of the k-slice method across various data sets. Additionally, we compare minmax distances obtained using the SlGi heuristic with those from ES and MILP algorithms, as well as the optimal minmax distance. Results show that the SlGi heuristic achieves comparable minmax distances to algorithms in the literature and the known optimal solution. Notably, differences between the SlGi heuristic and the known optimal solution decrease with an increasing number of salesmen, accompanied by reduced execution time. Thus, the SlGi heuristic is expected to perfom better with a larger number of salesmen.

A robot-assisted adaptive communication recovery method in disaster scenarios

Communication recovery is necessary for rescue and reconstruction scenarios including earthquakes, typhoons, floods, etc. The rapid and stable communication link can provide efficient victims’ real-time information for the rescue process. However, traditional centralized communication links cannot traverse the further victims with information-sharing requirements. And the even communication link distribution leads to a load burden on the crowded victim area. Thus, we propose a three-layer architecture consisting of the emergency communication vehicle, backbone links, and branch links to rapidly recover communication via mobile robots. Then, considering victims’ distribution, an improved MaxMin distance algorithm is presented as the basis of robot dispatch. The relay probability of the link is also estimated with closed formulae. Finally, simulation results verify that our proposed algorithm can recover communication with lower delay and higher packet delivery ratio.

Aerodynamic multi-objective optimization on train nose shape using feedforward neural network and sample expansion strategy

Feedforward neural network (FNN) models with strong learning ability and prediction accuracy are crucial for optimization. This paper investigates the effects of the number of training samples and the hidden layers on the accuracy of the FNN model. Meanwhile, under the premise of a high space-fillingness degree, a sample expansion strategy based on the max–min distance criterion is proposed, which ensures that the expanded sample set completely contains the pre-expanded. The strategy can eliminate the interference of sample differences. Furthermore, the multi-objective optimization on the train nose shape is accomplished by minimizing the aerodynamic lift force of the tail car (LT), as well as the aerodynamic drag force of the head (DH) and tail car (DT) using the FNN model. The results indicate that the number of training samples has a greater impact on the prediction error of the FNN model than the number of hidden layers does. Prediction errors decrease as the number of training samples increases and then stabilise, the most accurate one is chosen for nose shape optimization. The DH, DT, and LT all have prediction errors of less than 2%. Compared with the original high-speed train, the DH, DT, and LT of the optimal model are reduced by 5.24%, 3.74%, and 2.61%, respectively. Meanwhile, the correlation analysis reveals that the height of the cab window and the horizontal profile have a significant impact on the aerodynamic characteristics of the high-speed train.

Max–min distance embedding for unsupervised hyperspectral image classification in the satellite Internet of Things system

In modern society, hyperspectral images have widely been used in many applications. In the earth observation system, hyperspectral images are acquired by hyperspectral sensors and transmitted to the cloud computing center for analysis, which are the part of satellite Internet of Things(IoT) to build a large scale land-cover surveillance system. Thus, during recent years, researchers throughout academia and industry have been advancing the classification of hyperspectral images. Luckily for swamped remote sensing researchers, new technology is allowing brands to extract useful knowledge using artificial intelligence to enhance supervised hyperspectral image classification technology. However, conventional methods that label and process hyperspectral images manually are labor and time-consuming. Hence, in this paper, we proposed an unsupervised hyperspectral image classification framework. The paper differs from the existing methods by three aspects: (1) We adopt multi-scale spatial features to enhance the discriminability of data. (2) We propose deep autoencoder with max–min distance embedding to extract features and reduce dimensions of data, which is more suitable for large-scale data clustering. (3) We apply k-means clustering method to get original classification, then optimize the results by the guided filter. Experimental results on two widely used hyperspectral data, Salinas and Pavia University, show the competitive performance obtained by the proposed framework compared with other approaches.

On the Performance Supremum of CFO Based Physical Layer Identification

Physical layer identification is an emerging technique that exploits physical layer features to identify wireless devices. The identification accuracy and the device quantity that can be identified at most are significant for the identification scheme. Existing works primarily focus on the feature correlation analysis for multifeature selection without investigating the least upper bound (supremum) of the performance of a single feature. The supremum indicates the limit of the performance, which is another sight for evaluating the quality of features and improving the performance of the identification scheme. Therefore, this paper first investigates the supremum of the performance of the most commonly used physical layer feature, i.e., carrier frequency offset (CFO). Specifically, we offer a rigorous mathematical analysis and derive the closed-form expression of the supremum of identification accuracy based on the max-min distance analysis (MMDA) criterion. And then, the supremum of the number of distinguishable devices is also analyzed. Finally, we conducted a simulation study to verify the theoretical analysis result.

An Improved K-Means Algorithm Based on Min-Max Distance and BWP Metrics

The k-means algorithm is a conventional unsupervised cluster analysis algorithm, which is fast and easy to implement. Still, the number of clusters needs to be defined, and selecting the centre of mass is uncertain. A K-means algorithm based on the combination of maximum-minimum distance and Between-Within-Proportion (BWP) metrics is proposed to overcome these limitations. The results of simulation experiments on three datasets in the UCI database show that the proposed algorithm outperforms both the conventional K-means algorithm and the maximum-minimum distance-based K-means algorithm in terms of accuracy and clustering effect.

Maxmin distance sort heuristic-based initial centroid method of partitional clustering for big data mining

Maxmin distance sort heuristic-based initial centroid method of partitional clustering for big data mining

An Optimized k-means Algorithm Based on Information Entropy

Abstract Clustering is a widely used technique in data mining applications and various pattern recognition applications, in which data objects are divided into groups. K-means algorithm is one of the most classical clustering algorithms. In this algorithm, the initial clustering centers are randomly selected, this results in unstable clustering results. To solve this problem, an optimized algorithm to select the initial centers is proposed. In the proposed algorithm, dispersion degree is defined, which is based on entropy. In the algorithm, all the objects are firstly grouped into a big cluster, and the object that has the maximum dispersion degree and the object that has the minimum dispersion degree are selected as the initial clustering centers from the initial big cluster. And then other objects in the biggest cluster are partitioned to the initial clusters to which the objects are nearest. The partition process will be repeated until the cluster number is equal to the specified value k. Finally, the partitioned k clusters and their cluster centers are applied to k-means algorithm as initial clusters and centers. Several experiments are conducted on real data sets to evaluate the proposed algorithm. The proposed algorithm is compared with traditional k-means algorithm and max-min distance clustering algorithm, and experimental results show that the improved k-means algorithm is stable in selecting initial clustering, because it can select unique initial clustering centers. The optimized algorithm’s effectiveness and feasibility are also verified by experiments, and the algorithm can reduce the times of iterations and has more stable clustering results and higher accuracy.

Multi-Dimensional Polarized Modulation for Land Mobile Satellite Communications

In this article, a novel multiple-input multiple-out (MIMO) transmission scheme, called generalized polarized enhanced spatial modulation (GPESM), is proposed for dual-polarized land mobile satellite (LMS) communications. We first introduce the enhanced spatial modulation (ESM) technique for dual-polarized LMS communications, in which polarization dimension, spatial dimension and multiple signal constellations are used to transmit information and obtain substantial performance gain. Meanwhile, the theoretical upper bound for the average bit error probability (ABEP) of the proposed GPESM scheme is derived. In order to further improve the reliability of the system, we also propose two novel power allocation (PA) algorithms for GPESM system, which are the optimization-driven approximated max-min distance (AMMD)-based PA algorithm and the data-driven deep neural network (DNN)-based PA algorithm. To achieve an enhanced spatial diversity gain, we consider to apply a reconfigurable intelligent surface (RIS) to the GPESM system as a relay to assist in transmitting information. In this way, the user can receive the information transmitted by the satellite on one hand, and the information sent by the satellite via the RIS relay on the other hand. We also extend the above-mentioned two PA algorithms to the RIS-assisted GPESM systems. Our simulation results show that the RIS-assisted GPESM systems are capable of obtaining high bit error rate (BER) performance gain (up to 10 dB) compared to the standard GPESM system and two PA algorithms can further improve the performance to the systems.

K-Means Clustering Algorithm Based on Chaotic Adaptive Artificial Bee Colony

K-Means Clustering is a popular technique in data analysis and data mining. To remedy the defects of relying on the initialization and converging towards the local minimum in the K-Means Clustering (KMC) algorithm, a chaotic adaptive artificial bee colony algorithm (CAABC) clustering algorithm is presented to optimally partition objects into K clusters in this study. This algorithm adopts the max–min distance product method for initialization. In addition, a new fitness function is adapted to the KMC algorithm. This paper also reports that the iteration abides by the adaptive search strategy, and Fuch chaotic disturbance is added to avoid converging on local optimum. The step length decreases linearly during the iteration. In order to overcome the shortcomings of the classic ABC algorithm, the simulated annealing criterion is introduced to the CAABC. Finally, the confluent algorithm is compared with other stochastic heuristic algorithms on the 20 standard test functions and 11 datasets. The results demonstrate that improvements in CAABA-K-means have an advantage on speed and accuracy of convergence over some conventional algorithms for solving clustering problems.

An efficient computational algorithm for Hausdorff distance based on points-ruling-out and systematic random sampling

This paper proposes a novel algorithm for fast and accurate Hausdorff distance (HD) computation. The Hausdorff distance is used to measure the similarity between two point sets in various applications. However, it is hard to compute the HD algorithm efficiently between very large-scale point sets while ensuring the accuracy of the HD. The directed HD algorithm has two loops (called the outer loop and the inner loop) for calculating MAX-MIN distance, and the state-of-the-art algorithms, such as the Early break method and the Diffusion search method, focused on reducing the iterations of the inner loop. Our algorithm, however, concentrates on reducing the iterations of the outer loop. The proposed method simultaneously computes the temporary HD and temporary minimum distances of points corresponding to the outer loop using the opposite HD computation with very small systematic samples. Thereafter, a strategy of ruling out is employed to exclude non-contributing points. The new approach reduces the problems of different grid sizes and highly overlapping point sets as well as the very large-scale point sets. 3-D point clouds and real brain tumor segmentation (MRI 3-D volumes) are used for comparing the performance of the proposed algorithm and the state-of-the-art HD algorithms. In experimental results with 3-D point clouds, the proposed method is more than at least 1.5 times as faster as the compared algorithms. And, in experimental results with MRI 3-D volumes, the proposed method achieves a better performance than the compared algorithms over all pairs regardless of the grid size. Thus, as a whole, the proposed algorithm outperforms the compared algorithms.

Research on parallel data processing of data mining platform in the background of cloud computing

Abstract The efficient processing of large-scale data has very important practical value. In this study, a data mining platform based on Hadoop distributed file system was designed, and then K-means algorithm was improved with the idea of max-min distance. On Hadoop distributed file system platform, the parallelization was realized by MapReduce. Finally, the data processing effect of the algorithm was analyzed with Iris data set. The results showed that the parallel algorithm divided more correct samples than the traditional algorithm; in the single-machine environment, the parallel algorithm ran longer; in the face of large data sets, the traditional algorithm had insufficient memory, but the parallel algorithm completed the calculation task; the acceleration ratio of the parallel algorithm was raised with the expansion of cluster size and data set size, showing a good parallel effect. The experimental results verifies the reliability of parallel algorithm in big data processing, which makes some contributions to further improve the efficiency of data mining.

Balanced multiple weighted linear discriminant analysis and its application to visual process monitoring

Abstract Visual process monitoring is important in complex chemical processes. To address the high state separation of industrial data, we propose a new criterion for feature extraction called balanced multiple weighted linear discriminant analysis (BMWLDA). Then, we combine BMWLDA with self-organizing map (SOM) for visual monitoring of industrial operation processes. BMWLDA can extract the discriminative feature vectors from the original industrial data and maximally separate industrial operation states in the space spanned by these discriminative feature vectors. When the discriminative feature vectors are used as the input to SOM, the training result of SOM can differentiate industrial operation states clearly. This function improves the performance of visual monitoring. Continuous stirred tank reactor is used to verify that the class separation performance of BMWLDA is more effective than that of traditional linear discriminant analysis, approximate pairwise accuracy criterion, max–min distance analysis, maximum margin criterion, and local Fisher discriminant analysis. In addition, the method that combines BMWLDA with SOM can effectively perform visual process monitoring in real time.

Adaptive random testing with total cartesian distance for black box circuit under test

<p>Testing and verification of digital circuits is of vital importance in electronics industry. Moreover, key designs require preservation of their intellectual property that might restrict access to the internal structure of circuit under test. Random testing is a classical solution to black box testing as it generates test patterns without using the structural implementation of the circuit under test. However, random testing ignores the importance of previously applied test patterns while generating subsequent test patterns. An improvement to random testing is Antirandom that diversifies every subsequent test pattern in the test sequence. Whereas, computational intensive process of distance calculation restricts its scalability for large input circuit under test. Fixed sized candidate set adaptive random testing uses predetermined number of patterns for distance calculations to avoid computational complexity. A combination of max-min distance with previously executed patterns is carried out for each test pattern candidate. However, the reduction in computational complexity reduces the effectiveness of test set in terms of fault coverage. This paper uses a total cartesian distance based approach on fixed sized candidate set to enhance diversity in test sequence. The proposed approach has a two way effect on the test pattern generation as it lowers the computational intensity along with enhancement in the fault coverage. Fault simulation results on ISCAS’85 and ISCAS’89 benchmark circuits show that fault coverage of the proposed method increases up to 20.22% compared to previous method.</p>

Evaluation of Progressive Damage and Identification of Failure Mechanisms in Carbon Fiber Woven Composites via Tensile Test

Due to the widespread application of carbon fiber woven composites in aerospace, automobile and industrial area, understanding the damage evolution of carbon fiber woven composites are the most important aspect with respect to service life. Damage initiation and propagation of composites require greater mass force and little force can cause the failure of the structure on composite materials once exceeding the critical threshold at the onset of damage. A better knowledge of dynamic behaviors for damage growth can help to improve structural optimization design and production of high-performance composites. In this study, the mechanical behaviors of composites were analyzed under uniaxial tensile loading tests. Simultaneously, the chronology of internal damage evolution process in composite was detected by acoustic emission (AE). The AE signals recorded is filtered by the Maxmin Distance algorithm and following the k-means algorithm was used to achieve damage modes recognition. Based on microscopic observations and frequency range, the three clusters are correlated with different damage mode like matrix cracking, fiber/matrix debonding and fiber breakage. In addition, the Sentry Function (SF) affected by the mechanical behavior of the material was adopted to investigate evolution of the acoustic events and verify the results of clustering results.

Rapid Fine-Grained Classification of Butterflies Based on FCM-KM and Mask R-CNN Fusion

Butterfly recognition is a key link in the field of animal and plant observation. In order to realize the location and recognition of butterflies by robot vision system in complex environment, rapid fine-grained classification of butterflies based on the FCM-KM and Mask R-CNN fusion was proposed. First, an adaptive image enhancement algorithm based on fuzzy sets optimized by FOA was used to realize the adaptive fuzzy enhancement of butterfly images in image pre-processing. Then, K-Means clustering algorithm optimized by dynamic population firefly algorithm based on chaos theory and max-min distance algorithm, FCM-KM, was used to determine the optimal clustering number K instead of manual parameter tuning. Finally, while effectively segmenting the butterfly images, the Softmax in Mask R-CNN was used to classify the butterfly images. The recognition accuracy of the trained model in the verification set was 83.62%. To verify the feasibility and effectiveness of the model in complex environment, the rapid fine-grained classification method of butterflies based on the FCM-KM and Mask R-CNN fusion was compared with CNN, Resnet and original Mask R-CNN. The experiment results show that the method proposed in this paper has a good classification effect on butterflies in complex environment.

Parallel Implementation of Improved K-Means Based on a Cloud Platform

In order to solve the problem of traditional K-Means clustering algorithm in dealing with large-scale data set, a Hadoop K-Means (referred to HKM) clustering algorithm is proposed. Firstly, according to the sample density, the algorithm eliminates the effects of noise points in the data set. Secondly, it optimizes the selection of the initial center point using the thought of the max-min distance. Finally, it uses a MapReduce programming model to realize the parallelization. Experimental results show that the proposed algorithm not only has high accuracy and stability in clustering results, but can also solve the problems of scalability encountered by traditional clustering algorithms in dealing with large scale data.