Optimization Clustering Algorithm Research Articles

An Improved Boosting Bald Eagle Search Algorithm with Improved African Vultures Optimization Algorithm for Data Clustering

An Improved Boosting Bald Eagle Search Algorithm with Improved African Vultures Optimization Algorithm for Data Clustering

Kalman filtering and sine arithmetic optimization algorithm (KSAOA) for wireless sensor network clustering

Hierarchical routing is a crucial approach for enhancing the energy efficiency of Wireless Sensor Networks (WSN). The selection of sensor clusters poses a Non-deterministic Polynomial (NP) problem, typically addressed using meta-heuristic algorithms. However, existing research has not adequately considered the observation error of node energy and the limited convergence performance of their meta-heuristic algorithms. To address these challenges, we proposes a novel approach called KSAOA (Kalman filtering and Sine Arithmetic Optimization Algorithm) for WSN clustering, aimed at prolonging the service lifetime and enhancing the network balance of WSN. The proposed model combines Kalman filtering for estimating node energy and the Sine Arithmetic Optimization Algorithm (SAOA) for clustering nodes. SAOA builds upon the Arithmetic Optimization algorithm (AOA) by introducing a sine factor to extend the exploration boundary and a levy flight strategy to improve local exploration. Additionally, greedy algorithms are employed in the data transfer phase to enhance operational efficiency. The experimental results demonstrate the effectiveness of the proposed method in tabular and graphical form. The results are analyzed through comparative experiments to show that the proposed strategy is effective in improving the balance of network lifetime and network energy consumption.

A meta-heuristic approach for reliability-based design optimization of shell-and-tube heat exchangers

This study introduces a new framework for optimizing Shell-and-Tube Heat Exchanger (STHE) layouts using a reliability-based design optimization (RBDO) approach. The framework combines a control variate-based surrogate model and a hybrid metaheuristic algorithm. The proposed hybrid algorithm, k-means clustering and whale optimization algorithm (kWOA), was evaluated using CEC’2020 and a case study for optimizing STHE design under static conditions. kWOA showed superior performance in minimizing STHE's total annual cost and solving benchmark functions effectively. In our case study, the RBDO framework optimized the STHE design under two scenarios with target failure probabilities of 1% and 5%, resulting in cost increases of 112% and 82%, respectively, compared to deterministic optimization (DO). The integration of the RBDO approach with the STHE mathematical model, considering factors like inlet flow temperatures, mass flow rates, and fouling resistance, demonstrated the framework's ability to balance the trade-off between cost and reliability under uncertainty. Hybrid control variate radial basis function (RBF) models and Monte Carlo Simulation (MCS) were used to assess safety levels, showing the RBDO framework's superiority in improving safety and significantly reducing failure probability from 89% to 1% and 5%. The RBDO framework offers a robust approach for designing STHEs that achieve optimal performance while ensuring high reliability under uncertain conditions.

Detecting community on social networks with fast and optimal online clustering algorithms

Detecting community on social networks with fast and optimal online clustering algorithms

Distributed privacy preservation for online social network using flexible clustering and whale optimization algorithm

Distributed privacy preservation for online social network using flexible clustering and whale optimization algorithm

Privacy preserving using joint 2 K-means clustering and coati optimization algorithm for online social networks

Privacy preserving using joint 2 K-means clustering and coati optimization algorithm for online social networks

Towards optimal path planning for sustainable data collection in real-time wireless sensor networks

ABSTRACT Data collection using mobile sink(s) has proven to reduce energy consumption and enhance the network lifetime of wireless sensor networks. Generally speaking, a mobile sink (MS) traverses the network region, sojourning at rendezvous points (RPs) and gathering data there from nearby sensor nodes (SNs). However, determining the optimal number and location of RPs is a hard problem. Metaheuristic algorithms are well suited to solve such kind of problems. In this paper, we propose a tour planning scheme for the MS by a combined approach that uses K-means clustering and Particle Swarm Optimization algorithm. The proposed scheme first determines a set of near-optimal RPs and then uses them to schedule the tour of the MS. Herein, the energy consumption of the overall scheme is reduced due to the low computational cost of the proposed algorithm. At last, the proposed scheme is shown to outperform the comparative algorithms through simulation.

O‐SCDR: Optimal cluster with attention based shared‐account cross‐domain sequential recommendation using deep reinforcement learning technique

AbstractSequential recommendation involves suggesting subsequent items in a series of user activities. When recommending relevant items to users within the same account, the challenge lies in discerning diverse user behaviours to provide tailored recommendations based on individual preferences and timing. Cross‐domain sequential recommendation (CDSR) focuses on accurately extracting cross‐domain user preferences from both within‐sequence and between‐sequence interactions among items. Current approaches typically concentrate on learning preferences within a single domain using intra‐sequence item interactions, followed by a transfer module for cross‐domain preferences. However, this sequential process and implicit method are constrained by the effectiveness of the transfer module and may overlook inter‐sequence item associations. In this study, we propose an optimal cluster with attention‐based shared‐account cross‐domain sequential recommendation (O‐SCSR) system using deep reinforcement learning techniques. Our approach commences by formulating a modified hummingbird optimization (MHO) algorithm for clustering, effectively identifying latent users who share the same account to enhance the understanding of user interactions within shared‐account scenarios. Additionally, we design a domain filter based on quantum classic deep reinforcement learning (QCDRL), intelligently selecting interactions contributing to O‐SCSR. By quantifying rewards from transferred domain knowledge, the QCDRL‐based filter retains only valuable interactions for the task of SCDR. Finally, we validate the efficacy of our proposed O‐SCDR method using real‐world datasets, namely HVIDEO and HAMAZON. Through simulation results comparing the O‐SCDR system with existing state‐of‐the‐art systems, we demonstrate its effectiveness and legitimacy.

Big data clustering using fuzzy based energy efficient clustering and MobileNet V2

Big data analytics (BDA) is a systematic way to analyze and detect various patterns, relationships, and trends in vast amounts of data. Big data analysis and processing require significant effort, techniques, and equipment. The Hadoop framework software uses the MapReduce approach to do large-scale data analysis using parallel processing in order to generate results as soon as possible. Due to the traditional algorithm’s longer execution time and difficulty in processing big amounts of data, this is one of the main issues. Clusters are highly correlated inside each other but are not highly correlated with one another. The technique of effectively allocating limited resources is known as an optimization algorithm for clustering. For processing large amounts of data with several dimensions, the conventional optimization approach is insufficient. By using a fuzzy method, this can be prevented. In this paper, we proposed Fuzzy based energy efficient clustering approach to enhance the clustering mechanism. In summary, Fuzzy based energy efficient clustering introduces a function that measures the distance between the cluster center and the instance, which aids in improved clustering, and we then present the MobileNet V2 model to improve efficiency and speed up computation. To enhance the method’s performance and reduce its time complexity, the distributed database simulates the shared memory space and parallelizes on the MapReduce framework on the Hadoop cloud computing platform. The proposed approach is evaluated using performance metrics such as Accuracy, Precision, Adjusted Rand Index (ARI), Recall, F1-Score, and Normalized Mutual Information (NMI). The experimental findings indicate that the proposed approach outperforms the existing techniques in terms of clustering accuracy.

Distributed photovoltaic siting optimisation considering load centre similarity

The load fluctuation law remains challenging for traditional distribution line users. Furthermore, as the number of distributed photovoltaic installations connected to the distribution network rises, the issue of voltage overload on the distribution lines becomes more prevalent. This, in turn, adds complexity to power system operation. To configure the PV access location, whilst ensuring the line voltage is qualified, this paper applies the DBSCAN density clustering and K-means optimization algorithm to classify the distribution network line load. Different characteristics of the load collection are then constructed, and the concept of the load centre distance is proposed. PV access points are selected based on the load center characteristic curve and PV curve similarity. Model simulation is used to verify the conclusions, which demonstrate the rationality and effectiveness of the proposed method. The model simulation is conducted to confirm the soundness and credibility of the conclusions drawn.

Data survivability in unattended wireless sensor networks with optimal clustering and hybrid encryption algorithm

Data survivability in unattended wireless sensor networks with optimal clustering and hybrid encryption algorithm

Energy Efficient Clustering Using Improved Particle Swarm Optimization in Wireless Sensor Networks

The Wireless Sensor Network (WSN) includes many low-cost nodes that have the capacity to perceive, operate, and communicate wirelessly. WSN can spread the information to all around through a cooperative node approach. It also has many advantages in terms of both cost and cooperative intelligence. In a wireless sensor network, nodes have limited energy resources, so their life cycle is considered as one of the main concerns about wireless sensor networks. Energy efficiency grouping and routing are two well-known issues in optimization that have been widely studied in order to increase the lifetime of wireless sensor networks. In this paper, an improved particle swarm optimization (IPSO) clustering algorithm for energy efficiency network management is introduced in order to find a route for creating optimal clusters. To evaluate the efficiency of the proposed clustering algorithm, this algorithm is simulated and compared with the particle swarm optimization(PSO) algorithm based on parameters such as network energy, number of live nodes and network life.

Video Refereeing Model of Soccer Match Based on Fuzzy Clustering and Cuckoo Optimization Algorithm

Video Refereeing Model of Soccer Match Based on Fuzzy Clustering and Cuckoo Optimization Algorithm

A smart intelligent approach based on hybrid group search and pelican optimization algorithm for data stream clustering

A smart intelligent approach based on hybrid group search and pelican optimization algorithm for data stream clustering

INDIVIDUAL TREE-BASED FOREST SPECIES DIVERSITY ESTIMATION USING UAV-BORNE HYPERSPECTRAL AND LIDAR DATA

Abstract. Forest biodiversity is essential in maintaining ecosystem functions and services. Recently, unmanned aerial vehicle (UAV) remote sensing technology has emerged as a cost-effective and flexible tool for biodiversity monitoring. In this study, we compared the optimal clustering algorithm, classification method (spectral angle mapper, SAM), spectral diversity metric and structural heterogeneity index for forest species diversity estimation in two complex subtropical forests, Mazongling (MZL) and Gonggashan (GGS) National Nature Forest Reserves in China, using UAV-borne hyperspectral and LiDAR data. The results showed that the SAM classification method performed better with higher values of R2 than the clustering algorithm for predicting both species richness (MZL: 0.62 > 0.46 and GGS: 0.55 > 0.46) and Shannon-Wiener index (MZL: 0.64 > 0.58 and GGS: 0.52 > 0.47), while the optimal clustering algorithm had the highest prediction accuracy for the Simpson index, followed by the SAM classification method, spectral diversity metric and structural heterogeneity index (MZL: 0.83>0.44>0.31>0.12, GGS: 0.62>0.44>0.38>0.00). Our study indicated that the SAM classification method had the advantage of identifying rare species and estimating species richness, while the clustering method could capture forest diversity patterns rapidly without distinguishing the specific tree species and predict the Simpson index more accurately. Overall, both clustering and classification methods exhibited superior performance compared to spectral or structural diversity indices. Our findings highlight the applicability of UAV remote sensing in monitoring forest species diversity in complex subtropical forests.

Computational Pipeline for Analysis of Biomedical Networks with BioNAR.

In a living cell, proteins interact to assemble both transient and constant molecular complexes, which transfer signals/information around internal pathways. Modern proteomic techniques can identify the constituent components of these complexes, but more detailed analysis demands a network approach linking the molecules together and analyzing the emergent architectural properties. The Bioconductor package BioNAR combines a selection of existing R protocols for network analysis with newly designed original methodological features to support step-by-step analysis of biological/biomedical . Critically, BioNAR supports a pipeline approach whereby many networks and iterative analyses can be performed. Here we present a network analysis pipeline that starts from initiating a network model from a list of components/proteins and their interactions through to identifying its functional components based solely on network topology. We demonstrate that BioNAR can help users achieve a number of network analysis goals that are difficult to achieve anywhere else. This includes how users can choose the optimal clustering algorithm from a range of options based on independent annotation enrichment, and predict a protein's influence within and across multiple subcomplexes in the network and estimate the co-occurrence or linkage between metadata at the network level (e.g., diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms). The package is freely available in Bioconductor release 3.17: https://bioconductor.org/packages/3.17/bioc/html/BioNAR.html. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Creating and annotating the network Support Protocol 1: Installing BioNAR from RStudio Support Protocol 2: Building the sample network from synaptome.db Basic Protocol 2: Network properties and centrality Basic Protocol 3: Network communities Basic protocol 4: Choosing the optimal clustering algorithm based on the enrichment with annotation terms Basic Protocol 5: Influencing network components and bridgeness Basic Protocol 6: Co-occurrence of the annotations.

Study on smart grid fault detection based on ZigBee and MapX technologies

As the complexity of the power system and the increasing demand for electricity continue to rise, ensuring the reliability and stability of the power system becomes critically important. This study utilizes ZigBee communication modules to establish a sensor network for real-time monitoring of the power system's status and employs MapX technology to achieve geographic information visualization of the power system. Through an improved RBF-PCA-WFCM algorithm, grid data can be dimensionally reduced, enabling simulation training tailored to grid loads, resulting in more accurate algorithm analysis and optimization. This paper introduces fault localization and diagnostic methods, as well as clustering optimization algorithms, to achieve fault monitoring, analysis, and identification in the smart power grid. Research findings demonstrate that the intelligent power grid data analysis and fault detection system based on ZigBee and MapX technologies can enhance the reliability of the power system and better support the increasing demands of the evolving field of electricity.

Improving Wind Power Generation Forecasts: A Hybrid ANN-Clustering-PSO Approach

This study introduces a novel hybrid forecasting model for wind power generation. It integrates Artificial Neural Networks, data clustering, and Particle Swarm Optimization algorithms. The methodology employs a systematic framework: initial clustering of weather data via the k-means algorithm, followed by Pearson’s analysis to pinpoint pivotal elements in each cluster. Subsequently, a Multi-Layer Perceptron Artificial Neural Network undergoes training with a Particle Swarm Optimization algorithm, enhancing convergence and minimizing prediction discrepancies. An important focus of this study is to streamline wind forecasting. By judiciously utilizing only sixteen observation points near a wind farm plant, in contrast to the complex global numerical weather prediction systems employed by the European Center Medium Weather Forecast, which rely on thousands of data points, this approach not only enhances forecast accuracy but also significantly simplifies the modeling process. Validation is performed using data from the Italian National Meteorological Centre. Comparative assessments against both a persistence model and actual wind farm data from Southern Italy substantiate the superior performance of the proposed hybrid model. Specifically, the clustered Particle Swarm Optimization-Artificial Neural Network-Wind Forecasting Method demonstrates a noteworthy improvement, with a reduction in mean absolute percentage error of up to 59.47% and a decrease in root mean square error of up to 52.27% when compared to the persistence model.

Breast cancer segmentation using a hybrid AttendSeg architecture combined with a gravitational clustering optimization algorithm using mathematical modelling

Abstract Breast cancer diagnosis relies on breast ultrasound (BUS) and the early breast cancer screening saves lives. Computer-aided design (CAD) tools diagnose tumours via BUS tumour segmentation. Thus, breast cancer analysis automation may aid radiologists. Early detection of breast cancer might help the patients to survive and in context with this many approaches have been demonstrated by different researches, however, some of the works are weak in the segmentation of breast cancer images. to tackle these issues, this study propose a novel Hybrid Attendseg based gravitational clustering optimization (HA-GC) method which is utilized to segment breast cancer as normal malignant, and benign. For this we have taken the dataset known as breast ultrasound (BUS) images. This method constructively segments the breast cancer images. Prior to the segmentation, pre-processing is carried out which can be used to normalize the images incorporated with the removal of unwanted noises and format the images Optimization selects the best qualities. An experiment is conducted and compared the results with the parameters such as Dice coefficient, Jacquard, Precision, and Recall and attained over 90% and ensures the usage of present work in the segmentation of breast cancer images.

Modeling of farmers’ production performance portrait based on GASA-WFCM clustering

To identify the typical characteristics of farmers with different production performance, this paper constructs a farmer portrait model from the perspective of production performance segmentation. Firstly, we constructed the evaluation index system of production performance and quantified grape farmers’ production performance. Then, an optimized clustering algorithm (GASA-WFCM) was proposed to cluster the grape farmers based on their production performance, which was based on fuzzy C-mean algorithm, and incorporating the genetic simulated annealing algorithm to overcome the problem of sensitive initial clustering centers and tendency to fall into local optimum, and the ReliefF algorithm was also integrated to strengthen the contribution ability of effective features to clustering. Finally, we designed a labeling system and feature transformation quantification method, and combined with the visualization effects to realize the description of grape farmers’ portraits. The results of models training indicated that the convergence speed and division accuracy of the optimized clustering algorithm were significantly improved. By comparing the clustering results on the typical UCI datasets, it was demonstrated that GASA-WFCM algorithm improved the accuracy, NMI and ARI by 3.0%, 7.2% and 6.1%, respectively. Moreover, the segmentation method was applied in grape farmers dataset, and the findings revealed that Chinese fresh grape farmers may be clustered into three groups, namely high-yield-driven grape farmers, high-input-driven grape farmers and high -income grape farmers, respectively. This paper provided a set of methods of users clustering analysis and portrait from a theoretical point of view. The findings and suggestions can help provide accurate and focused decision support for improving the effect of agricultural technology promotion and farmers’ production performance.