Optimization Clustering Algorithm Research Articles

Identify Optimal Traffic Condition and Speed Limit for Hard Shoulder Running Strategy

Highway system is experiencing increasing traffic congestion with fast-growing number of vehicles in metropolitan areas. Implementing traffic management strategies such as utilizing the hard shoulder as an extra lane could increase highway capacity without extra construction work. This paper presents a method of determining an optimal traffic condition and speed limit of opening hard shoulder. Firstly, the traffic states are clustered using K-Means, mean shift, agglomerative and spectral clustering methods, and the optimal clustering algorithm is selected using indexes including the silhouette score, Davies-Bouldin Index and Caliski-Harabaz Score. The results suggested that the clustering effect of using K-Means method with three categories is optimal. Then, cellular automata model is used to simulate traffic conditions before and after the hard shoulder running strategy is applied. The parameters of the model, including the probabilities of random deceleration, slow start and lane change, are calibrated using real traffic data. Four indicators including the traffic volume, the average speed, the variance of speed, and the travel time of emergency rescue vehicles during traffic accident obtained using the cellular automata model are used to evaluate various hard shoulder running strategies. By using factor analysis and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods, the optimal traffic condition and speed limit of opening hard shoulder could be determined. This method could be applied to highway segments of various number of lanes and different speed limits to optimize the hard shoulder running strategy for highway management.

IACO‐ERP: An improved ACO‐based energy‐efficient routing protocol for fog‐based WSNs

SummaryWireless Sensor Networks (WSNs) present numerous constraints, which motivate many researchers and which reside mainly in the fact that their resources are limited in terms of communication, calculations, and energy. In particular, the stress linked to energy is considered as a fundamental problem. Indeed, all the sensor elements need energy to function. Thus, controlling the energy consumption of a node remains a major problem for maximizing the lifetime of the network. The clustering and Ant Colony Optimization (ACO) algorithms have been seen as a good solution to route data while maintaining a better network lifetime. However, the existing state‐of‐the‐art cluster and ACO‐based routing protocols perform re‐clustering and fail to restrict the ant searching scope. This consumes a high amount of energy and makes the convergence speed slow. For these reasons, this paper presents an Improved ACO‐based Energy‐efficient Routing Protocol (IACO‐ERP) to mitigate the hot spot problem by proposing a multiple Fog Nodes (FNs)‐based energy‐efficient network model. IACO‐ERP avoids re‐clustering and rotates the Cluster Head (CH) role based on energy among cluster members without any need for control overhead, and also, it proposes to limit the scope of ant searching for optimal route toward the sink based on some specific parameters such as energy and distance, thus avoiding routing loop, slow convergence speed, and excessive energy consumption. The proposed routing protocol is evaluated against some recent and relevant existing solutions. The simulations results reveal that IACO‐ERP provides significant improvement in comparison to its counterparts in terms of various main metrics.

Data Disseminated Energy-Efficient Clustering Algorithm For Avoid Load and Bandwidth Consumption In WSN Integrated IoT Platform

In the current scenario, wireless sensor networks (WSNs) are embedded in the “Internet of Things (IoT) ” platform where sensor nodes automatically link and use the Internet to communicate and execute their activities. WSNs are well suited for the collection of long-term IoT representation environmental data. The WSNs includes wireless communication capabilities, computation process, and nodes with sensing capabilities. Data dissemination methods, power management, and many routing procedures have been mainly designed for WSNs integrated IoT platform. Also, we consider load and bandwidth consumption as an essential issue in our design. Hence, this paper introduces a data disseminated energy-efficient clustering algorithm using multiple parameter decision-making for selecting an optimal clustering algorithm. For the cluster head selection process, we consider different kinds of parameters such as Initial Energy, Average Energy of the Network, Energy Consumption Rate, and Residual Energy. By considering these factors, nodes are continually monitored, and the cluster header is selected according to the maximum energy value. The respective cluster members are chosen in the cluster coverage area using the swarming techniques. In other words, we used swarm techniques as a cluster head selection process to avoid load and bandwidth consumption. The excellence of the system is evaluated using simulation results which show that this introduced method is more effective in terms of preventing bandwidth and load consumption. In this context, we use network simulator 2 (NS2) to simulate different kinds of metrics such as a packet delivery ratio, network lifetime, and energy consumption.

Using single-cell cytometry to illustrate integrated multi-perspective evaluation of clustering algorithms using Pareto fronts.

Many 'automated gating' algorithms now exist to cluster cytometry and single cell sequencing data into discrete populations. Comparative algorithm evaluations on benchmark datasets rely either on a single performance metric, or a few metrics considered independently of one another. However, single metrics emphasise different aspects of clustering performance and do not rank clustering solutions in the same order. This underlies the lack of consensus between comparative studies regarding optimal clustering algorithms and undermines the translatability of results onto other non-benchmark datasets. We propose the Pareto fronts framework as an integrative evaluation protocol, wherein individual metrics are instead leveraged as complementary perspectives. Judged superior are algorithms that provide the best trade-off between the multiple metrics considered simultaneously. This yields a more comprehensive and complete view of clustering performance. Moreover, by broadly and systematically sampling algorithm parameter values using the Latin Hypercube sampling method, our evaluation protocol minimises (un)fortunate parameter value selections as confounding factors. Furthermore, it reveals how meticulously each algorithm must be tuned in order to obtain good results, vital knowledge for users with novel data. We exemplify the protocol by conducting a comparative study between three clustering algorithms (ChronoClust, FlowSOM and Phenograph) using four common performance metrics applied across four cytometry benchmark datasets. To our knowledge, this is the first time Pareto fronts have been used to evaluate the performance of clustering algorithms in any application domain. Implementation of our Pareto front methodology and all scripts to reproduce this article are available at https://github.com/ghar1821/ParetoBench.

A Group Decision Optimization Analogy-Based Deep Learning Architecture for Multiclass Pathology Classification in a Voice Signal

Non-invasive identification of abnormal voice using feature descriptors and machine learning classifiers has been the preference of many literatures. Using feature descriptors and time-frequency image with deep learning is a better alternative. Most voice pathology deep learning frameworks are based on binary classification model. Implementing a hardware system requires a network which can recognise the exact pathology. A thorough investigation of time-frequency analysis using a sophisticated deep learning system is required. Current work explores a non-invasive, robust and computationally non-intensive architecture for detecting multiclass laryngeal pathology. For applying in a realistic environment, the capability of a fully-connected network and a fully convolutional deep-learning voice denoiser network is initially investigated. Denoised training samples are used to create three different time-frequency image corpuses. These multivariate image datasets will train three improved variants of some state-of-the-art convolutional neural network models which use 3D convolution kernel. A “group decision analogy” technique is employed for training and attaining the global maxima of the current classification problem. The concept of group decision analogy stems from well-known clustering and swarm optimization algorithms. It uses three stages to optimise the predicted score. In the first level, these enhanced deep-learning models are trained using the three datasets to recognise healthy, hyperkinetic dysphonia, hypokinetic dysphonia, and reflux laryngitis classes. The prediction then undergoes a second and third stage. A score of 80.59% is obtained before using “group decision analogy” which is raised to 97.7% finally. The hypokinetic dysphonia and reflux laryngitis achieves 100% classification.

A Biologically Inspired Algorithm for Low Energy Clustering Problem in Body Area Network

The growing application of body area networks (BANs) in different fields makes the low energy clustering a paramount issue. A clustering optimization algorithm in BANs is a fundamental scheme to guarantee that the essential collected data can be forwarded in a reliable path and improve the lifetime of BANs. Low energy clustering is a technique, which provides a method that shows how to reduce network communication costs in BANs. A careful low energy clustering scheme is one of the most critical means in the research of BANs, which has attracted considerable attention, comprising monitoring capability constraints. However, the classical clustering method leads to high cost when constraints such as large overall energy consumption are undertaken. Hence, a binary immune hybrid artificial bee colony algorithm (BIHABCA), a randomized swarm intelligent scheme applied in BANs, motivated by immune theory and hybrid scheme is introduced. Furthermore, we designed the formulation that considers both distances between two nodes and the length of bits. Finally, we have compared the energy cost optimized by BIHABCA with a shuffled frog leaping algorithm, ant colony optimization, and simulated annealing in the simulation with different quantity of nodes in terms of energy cost. Results show that the energy cost of the network optimized by the proposed BIHABCA method decreased compared to those by the other three methods which mean that the proposed BIHABCA finds the global optima and reduces the energy cost of transmitting and receiving data in BANs.

Quantum approximate optimization and k-means algorithms for data clustering

Noisy intermediate-scale quantum (NISQ) devices are cutting-edge technology expected to demonstrate potential and advantages of quantum computing over classical computing. Its low number of qubits and imperfection from noises restrict running full-scale quantum algorithms on such devices; however, quantum advantages can still be obtained. To achieve quantum advantages from NISQ devices, the hybrid quantum-classical algorithms were introduced. Quantum approximate optimization algorithm (QAOA) is a variational hybrid algorithm, which utilizes a NISQ device as a sub-unit for specific tasks and performs most calculations on a classical computer. QAOA provides an approximate solution, with arbitrary precision as the number of operations increases, for optimization problems. In this work we investigate the possibility of applying QAOA to a clustering problem and compare its performance with the classical k-means algorithm. It turns out that the weights in graph connectivity can degrade the algorithm operation and make it more difficult to approximate the solution. We also benchmark the QAOA by comparing the approximated solutions with the exact one obtained from a classical clustering algorithm.

Energy Aware Enhanced Krill Herd Algorithm Enabled Clustering for Unmanned Aerial Vehicles

Recently, unmanned aerial vehicles (UAV) have gained maximum interest in diverse applications ranging from military to civilian areas. The presence of numerous energy-constrained UAVs in an adhoc manner poses several design issues. At the same time, the limited battery, high mobility, and adaptive characteristics of the UAVs need effective design of clustering techniques for UAVs. In this manner, this paper presents a levy flight with a krill herd optimization algorithm (LF-KHOA) for energy-efficient clustering in UAVs. The proposed LF-KHOA technique integrates the concepts of LF to the KHOA to enhance efficiency and search space exploration. In addition, the LF-KHOA technique derives a fitness function involving three input parameters to elect cluster heads (CHs) and organize clusters. The energy consumed by the UAVs depends on the distance from UAVs to nearby nodes. Therefore, the fitness function aims to decrease communication distance, which mitigates energy utilization when transmitting the information. To ensure the better performance of the LF-KHOA technique, an extensive set of simulations takes place, and the results are inspected in terms of different measures. The experimental results highlighted the betterment of the LF-KHOA technique over the current state of art techniques.

Application of a Novel PNN Evaluation Algorithm to a Greenhouse Monitoring System

Evaluating the quality of environments within greenhouses has been an important aspect of greenhouse agriculture. However, the existing greenhouse monitoring systems are generally unable to evaluate the quality of greenhouse environments, and at present, it is evaluated manually, which leads to the failure of timely and effective evaluation. Therefore, how to evaluate the greenhouse environment quality accurately, quickly, and automatically is an urgent issue to be tackled. Inspired by the optimization algorithm, clustering algorithm, and probabilistic neural network (PNN), this article proposes the application of a novel PNN evaluation algorithm to a decentralized greenhouse monitoring system to evaluate the environment quality in real time. First, the original training samples are clustered by an improved K-means clustering algorithm (called K-means- α), and the training samples near the cluster centroids are used as new ones to establish the PNN structure. Then, the particle swarm optimization (PSO) algorithm (whereby the fitness function is defined as the PNN's classification error rate on the test samples) is used to iteratively optimize smoothing factors adopted by different classes of pattern units in the PNN. The optimized PNN is finally obtained (called α-PSO-M-PNN). The experimental results demonstrate that compared with conventional PNN-based algorithms, the optimized PNN algorithm has the advantages of a simpler network structure, higher classification accuracy, and requiring fewer training samples. In addition, it is more suitable for the microprocessor-based monitoring system because its computational and storage requirements are within the limits of the microprocessor.

Fault Detection Method and Simulation Based on Abnormal Data Analysis in Wireless Sensor Networks

With the rapid development of Internet of things and information technology, wireless sensor network technology is widely used in industrial monitoring. However, limited by the architecture characteristics, software and hardware characteristics, and complex external environmental factors of wireless sensor networks, there are often serious abnormalities in the monitoring data of wireless sensor networks, which further affect the judgment and response of users. Based on this, this paper optimizes and improves the fault detection algorithm of related abnormal data analysis in wireless sensor networks from two angles and verifies the algorithm at the same time. In the first level, aiming at the problem of insufficient spatial cooperation faced by the network abnormal data detection level, this paper first establishes a stable neighbor screening model based on the wireless network and filters and analyzes the reliability of the network cooperative data nodes and then establishes the detection data stability evaluation model by using the spatiotemporal correlation corresponding to the data nodes. Realize abnormal data detection. On the second level, aiming at the problem of wireless network abnormal event detection, this paper proposes a spatial clustering optimization algorithm, which mainly clusters the detection data flow in the wireless network time window through the clustering algorithm, and analyzes the clustering data, so as to realize the detection of network abnormal events, so as to retain the characteristics of events and further classify the abnormal data events. This paper will verify the realizability and superiority of the improved optimization algorithm through simulation technology. Experiments show that the fault detection rate based on abnormal data analysis is as high as 97%, which is 5% higher than the traditional fault detection rate. At the same time, the corresponding fault false detection rate is low and controlled below 1%. The efficiency of this algorithm is about 10% higher than that of the traditional algorithm.

Improved Black Hole optimization algorithm for data clustering

Algorithms inspired by nature became more popular in the last few years. They showed up powerful capability in solving optimization problems. This capability was obtained by their ability to be applied individually or by merging them with other algorithms or techniques. The Black Hole optimization algorithm is a nature-inspired algorithm that belongs to the meta-heuristic category. The Black-Hole algorithm (BH) simulates the black hole phenomenon which is formed from a star with massive size and very high gravitational power. The algorithm starts with a population of a specific size of possible solutions and then gets evaluated by selecting the best one as a black hole. In the suggested modifications, we have introduced a new idea for generating the stars absorbed by the black hole. The star movement towards the black hole has also been modified to increase exploration capabilities. The modified algorithm was used to prove its effectiveness in data clustering without any prior knowledge about the nature of the provided data. Several benchmark datasets and statistical techniques have been used to evaluate the performance of suggested modification. The experiment results promised that the improved algorithm can overcome popular optimization algorithms.

Data analysis using representation theory and clustering algorithms

This work aims to expand the knowledge of the area of data analysis through persistence homology and representations of directed graphs. To be specific, we looked for how we can analyze homology cluster groups using agglomerative Hierarchical Clustering algorithms and methods. Additionally, the Wine data, which is offered in R studio, was analyzed using various cluster algorithms such as Hierarchical Clustering, K-Means Clustering, and PAM Clustering. The goal of the analysis was to find out which cluster's method is proper for a given numerical dataset. We tried to find the agglomerative hierarchical clustering method by testing the data that will be the optimal clustering algorithm among these three; K-Means, PAM, and Random Forest methods. By comparing each model's accuracy value with cultivar coefficients, we concluded that K-Means methods are the most helpful when working with numerical variables. On the other hand, PAM clustering and Gower with Random Forest are the most beneficial approaches when using categorical variables. These tests can determine the optimal number of clustering groups, given the data set, and by doing the proper analysis. Using those the project, we can apply our method to several industrial areas such that clinical, business, and others. For example, people can make different groups based on each patient who has a common disease, required therapy, and other things in the clinical society. Additionally, people can expect to get several clustered groups based on the marginal profit, marginal cost, or other economic indicators for the business area.

Quasi Oppositional Glowworm Swarm Optimization Algorithm for Energy Efficient Clustering in Wireless Sensor Networks

Wireless sensor network (WSN) becomes popular due to its applicability in distinct application areas like healthcare, military, search and rescue operations, etc. In WSN, the sensor nodes undergo deployment in massive number which operates autonomously in harsh environment. Because of limited resources and battery operated sensor nodes, energy efficiency is considered as a main design issue. To achieve, clustering is one of the effective technique which organizes the set of nodes into clusters and cluster head (CH) selection takes place. This paper presents a new Quasi Oppositional Glowworm Swarm Optimization (QOGSO) algorithm for energy efficient clustering in WSN. The proposed QOGSO algorithm is intended to elect the CHs among the sensor nodes using a set of parameters namely residual energy, communication cost, link quality, node degree and node marginality. The QOGSO algorithm incorporates quasi oppositional based learning (QOBL) concept to improvise the convergence rate of GSO technique. The QOGSO algorithm effectively selects the CHs and organizes clusters for minimized energy dissipation and maximum network lifetime. The performance of the QOGSO algorithm has been evaluated and the results are assessed interms of distinct evaluation parameters.

Iterative Sensor Clustering and Mobile Sink Trajectory Optimization for Wireless Sensor Network with Nonuniform Density

Sensor clustering and trajectory optimization are a hot topic for last decade to improve energy efficiency of wireless sensor network (WSN). Most of existing studies assume that the sensor is uniformly deployed or all regions in the WSN coverage have the same level of interest. However, even in the same WSN, areas with high probability of disaster will have to form a “hotspot” with more sensors densely placed in order to be sensitive to environmental changes. The energy hole can be serious if sensor clustering and trajectory optimization are formulated without considering the hotspot. Therefore, we need to devise a sensor clustering and trajectory optimization algorithm considering the hotspots of WSN. In this paper, we propose an iterative algorithm to minimize the amount of energy consumed by components of WSN named ISCTO. The ISCTO algorithm consists of two phases. The first phase is a sensor clustering phase used to find the suitable number of clusters and cluster headers by considering the density of sensor and residual battery of sensors. The second phase is a trajectory optimization phase used to formulate suitable trajectory of multiple mobile sinks to minimize the amount of energy consumed by mobile sinks. The ISCTO algorithm performs two phases repeatedly until the amount of energy consumed by the WSN is not reduced. In addition, we show the performance of the proposed algorithm in terms of the total amount of energy consumed by sensors and mobile sinks.

Application of big data optimized clustering algorithm in cloud computing environment in traffic accident forecast

As the usage rate of cars is getting higher and higher, the injuries and losses caused by traffic accidents are also getting bigger and bigger. If some traffic accidents can be predicted, then such losses can be greatly solved. Although there are abundant research results on intelligent transportation, there are not many research results on how to predict traffic accidents. For this issue, the main aim of this paper is to propose a continuous non-convex optimization of the K-means algorithm in order to solve the model problem in the traffic prediction process. First, this paper uses clustering algorithm for feature analysis and big data for the establishment of simulation model in cloud environment. Through this paper an equivalent model, using matrix optimization theory to analyze and process K-means problem, and design efficient and theoretically guaranteed algorithms for big data. By simulating the traffic situation in Shanghai city within three years, the outcomes display that the model endorsed in the given paper can predict traffic accidents at a rate of 93.88% and the accuracy rate of traffic accident processing time is 78%, which fully illustrates the effectiveness of the model established in this paper.

An improved artificial bee colony algorithm based on whale optimization algorithm for data clustering

Data clustering is one of the branches of unsupervised learning and it is a process whereby the samples are divided into categories whose members are similar to each other. The K-means algorithm is a simple and fast clustering technique, but it has many initial problems, for example, it depends heavily on the initial value for better clustering. Moreover, it is susceptible to outliers and unbalanced clusters. The artificial bee colony (ABC) algorithm is one of the meta-heuristic algorithms that is used nowadays to solve many optimization problems including clustering and the fundamental problem of this algorithm is exploration and late convergence. In this paper, to solve the problem of exploration and late convergence in ABC are used Random Memory (RM) and Elite Memory (EM) called ABCWOA algorithm. RM in the ABCWOA algorithm has used the search stage for the bait in the whale optimization algorithm (WOA) and EM is also used to increase convergence. In addition, we control the use of EM dynamically. Finally, the proposed method was implemented on ten standard datasets from the UCI Machine Learning Database for evaluation. Moreover, it was compared in terms of statistical criteria and analysis of variance (ANOVA) test with basic ABC and WOA, vortex search (VS) algorithm, butterfly optimization algorithm (BOA), crow search (CS) algorithm, and cuckoo search algorithm (CSA). The simulation results showed that the degree of convergence maintained its performance by increasing the number of repetitions of the proposed method, but the ABC algorithm has shown poor performance by increasing the repetition of performance. ANOVA results also confirmed that the ABCWOA algorithm has a positive effect on the population and it contains less noise than other comparative algorithms. The ABCWOA algorithm show that the ABCWOA algorithm performs better than other meta-heuristic algorithms.

Hybridizing Gray Wolf Optimization (GWO) with Grasshopper Optimization Algorithm (GOA) for text feature selection and clustering

Text analysis in the field of text mining requires complex techniques for handling several text documents. Text clustering is among the most effective tactics in the field of text mining, machine recruitment and pattern recognition. Computers can start organizing a corpus document in certain organizational structure of conceptual clusters using reasonable text-clustering method. Informative and un-informational functionalities of the text documents contain noisy, inconsequential and superfluous features. The main method of finding a new subset of informative feats for each document is the unsupervised selection of text features. The functional selection technique has two aims: (1) maximize text clustering algorithm reliability, (2) minimize the number of uninformative traits. The proposed technique is that it produces a mature convergence rate and requires minimal computational time and is trapped in local minima in a low dimensional space. The text data is fed as the input and pre-processing steps are performed in the document. Next, the text feature selection is processed by selecting the local optima from the text document and then selecting the best global optima from local optimum using hybrid GWO–GOA.​ Furthermore, the selected optima are clustered using the Fuzzy c-means (FCM) clustering algorithm. This algorithm improves the reliability and minimizes the computational time cost. Eight datasets are used in the proposed algorithm and the performance is envisaged efficaciously. The evaluation metrics used for performing text feature selection and text clustering are accuracy, precision, recall, F-measure, sensitivity, specificity and show better quality when comparing with various other algorithms. When comparing with GWO, GOA and the proposed hybrid GWO–GOA algorithm, the proposed methodology reveals 87.6% of efficiency.

A clustering algorithm based on the combination of screening strategy and swarm

aiming at the problem that the k-means clustering algorithm is affected by the initial clustering center, a k-means clustering optimization algorithm combining the screening strategy and artificial colony (ABC) was proposed.This algorithm operates in an unsupervised way, separating data from outlier points through screening, combining the advantages of ABC algorithm, using the objective function of ABC algorithm as the measurement function of initial clustering, and improving the effectiveness and accuracy of k-means clustering by changing the initial clustering center.

Intrusion Detection Based on Dynamic Gemini Population DE-K-mediods Clustering on Hadoop Platform

In view of the fact that the existing intrusion detection system (IDS) based on clustering algorithm cannot adapt to the large-scale growth of system logs, a K-mediods clustering intrusion detection algorithm based on differential evolution suitable for cloud computing environment is proposed. First, the differential evolution algorithm is combined with the K-mediods clustering algorithm in order to use the powerful global search capability of the differential evolution algorithm to improve the convergence efficiency of large-scale data sample clustering. Second, in order to further improve the optimization ability of clustering, a dynamic Gemini population scheme was adopted to improve the differential evolution algorithm, thereby maintaining the diversity of the population while improving the problem of being easily trapped into a local optimum. Finally, in the intrusion detection processing of big data, the optimized clustering algorithm is designed in parallel under the Hadoop Map Reduce framework. Simulation experiments were performed in the open source cloud computing framework Hadoop cluster environment. Experimental results show that the overall detection effect of the proposed algorithm is significantly better than the existing intrusion detection algorithms.

Phase I non-linear profiles monitoring using a modified Hausdorff distance algorithm and clustering analysis

PurposeThe purpose of this paper is to propose a new non-parametric phase I control chart for the problem of non-linear profile outlier detection.Design/methodology/approachThe proposed non-parametric method is based on a modified Hausdorff distance, which does not require a restrictive assumption on the form of profiles. By obtaining the distance between each profile and the baseline profile, the authors introduced an iterative optimization clustering algorithm to identify outliers by clustering distances.FindingsThe simulation results show that the proposed method can distinguish outliers for structural changes of non-linear profiles. The authors also present a real industrial case example to highlight how practitioners can implement and make use of the proposed control chart in outlier detection applications, and it achieves higher accuracy in the outlier detection of complex profiles.Practical implicationsThe research results of this paper can be applied to any manufacturing or service system whose quality characteristics are characterized by non-linear profiles. This new approach provides quality practitioners a better decision-making tool for non-linear profile outlier detection.Originality/valueDue to the complexity of real-world applications, the non-linear profiles monitoring problem is yet to be addressed. However, the related research still remains rare. And the authors’ proposed non-linear profile control chart, which does not require a restrictive assumption on the form of profiles, shows its applicability and superiority in simulation study and real-world case.