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This paper presents an in-depth study and analysis of large datasets of mixed and attribute features under Spark using a large dataset clustering algorithm. The classical algorithm K-means based on division and the density-based clustering algorithm DPC, which has become more popular in recent years, are selected as the research objects of this paper. Secondly, the original K-means algorithm is improved by combining holdout validation and K-means++ method to address the shortcomings of the K-means algorithm that the number of class clusters K needs to be set in advance and the initial class cluster centers are chosen randomly, which leads to unstable iterations and slow convergence of clustering results. The similarity matrix will be continuously updated during the iterative process. It mainly refers to the process of dividing objects into multiple classes according to the degree of similarity between objects. After the division, the objects within the class are like each other, while the objects between the classes are different from each other. The comparison experiments of the improved algorithm before and after the MovieLens dataset are conducted to verify that the new algorithm has better performance in terms of clustering accuracy and efficiency. Again, to address the drawback that the clustering results in the DPC algorithm rely heavily on the subjective selection of the truncation distance parameter cd, and it is difficult to handle datasets with complex distribution and large density variation, the algorithm can generate the optimal cd adaptively by combining K-nearest neighbors and introducing the distance comparison quantity, which has a better performance by considering the overall and local distribution of the data. The feasibility of the improved method is verified by validating the algorithm with artificial datasets and UCI datasets as well as separation tests. Finally, the parallelized design and implementation of the improved K-means algorithm and CDPC-KNN algorithm are completed by building a Spark clustering environment, and the parallelized algorithm is verified to have much better data processing capability and be more adaptable to the clustering analysis of large-scale data by comparing algorithm string parallelism experiments. [ABSTRACT FROM AUTHOR]

In view of the miniaturization and decentralization characteristics of agricultural equipment factories in China, agricultural equipment manufacturing is well suited to the cloud manufacturing model, but there is no specific research on cloud services optimization for it. To fill the research gap, a cloud service optimization method is proposed in this paper. For the optimization model, the dynamic coefficient strategy and the reliability feedback update strategy are added to the mathematical model to strengthen the applicability of farming season. As optimization algorithm, a dynamic artificial ant-bee colony algorithm (DAABA) based on artificial ant colony algorithm and bee colony algorithm is presented. The optimal fusion evaluation strategy is used to save optimization time by reducing the useless iteration, and the iterative adjustment threshold strategy is adopted to improve the accuracy of cloud service by increasing the size of bee colony. Finally, the performance of DAABA is verified to be more superior by comparing with other algorithms. [ABSTRACT FROM AUTHOR]

A fuzzy cloud resource scheduling model with time-cost constraints is built using fuzzy triangular numbers to represent uncertain task execution time. Task scheduling reduces total time and cost spent on a project. It connects virtual machines and functions. Particle swarm optimization (HPO) is used to plan cloud resources (HSOA). The approach uses orthogonal particle swarm initialization to increase the quality of the initial particle exploration, rerandomization to regulate the particle search range, and real-time updating of inertia weights to control particle speed. The suggested problem model and optimization approach are evaluated using random simulation data provided by the CloudSim simulation platform. Less overall execution time and a lower cost are shown to have fast convergence and solution capabilities in experiments. [ABSTRACT FROM AUTHOR]

With the rapid economic development and urbanization process accelerating, motor vehicle ownership in large cities is increasing year by year; urban traffic congestion, parking difficulties, and other problems are becoming increasingly serious; in ordinary daily life, continuous risk of disturbance, having a flexible transportation system network is more able to alleviate daily congestion in the city, and the main thing about flexible transportation network is its algorithm. It is worth noting that congestion in many cities is generally reflected in the main roads, while many secondary roads and branch roads are underutilized, and the limited road resources in cities are not fully utilized. As an economic and effective road traffic management measure, one-way traffic can balance the spatial and temporal distribution of traffic pressure within the road network, make full use of the existing urban road network capacity, and solve the traffic congestion problem. Therefore, it is of great theoretical and practical significance to develop a reasonable and scientific one-way traffic scheme according to the characteristics of traffic operation in different regions. Based on the fixed demand model, the influence of traffic demand changes is further considered, the lower-level model is designed as an elastic demand traffic distribution model, the excess demand method is used to transform the elastic demand problem into an equivalent fixed demand problem based on the extended network, and the artificial bee colony algorithm based on risk perturbation is designed to solve the two-level planning model. The case study gives a one-way traffic organization optimization scheme that integrates three factors, namely, the average load degree overload limit of arterial roads, the detour coefficient, and the number of on-street parking spaces on feeder roads, and performs sensitivity analysis on the demand scaling factor. [ABSTRACT FROM AUTHOR]

Cloud computing provides multiple services such as computational services, data processing, and resource sharing through multiple nodes. These nodes collaborate for all prementioned services in the data center through the head/leader node. This head node is responsible for reliability, higher performance, latency, and deadlock handling and enables the user to access cost-effective computational services. However, the optimal head nodes' selection is a challenging problem due to consideration of resources such as memory, CPU-MIPS, and bandwidth. The existing methods are monolithic, as they select the head nodes without taking the resources of the nodes. Still, there is a need for the candidate node which can be selected as a head node in case of head node failure. Therefore, in this paper, we proposed a technique, i.e., Head Node Selection Algorithm (HNSA), for optimal head node selection from the data center, which is based on the genetic algorithm (GA). In our proposed method, there are three modules, i.e., initial population generation, head node selection, and candidate node selection. In the first module, we generate the initial population by randomly mapping the task on different servers using a scheduling algorithm. After that, we compute the overall cost and the cost of each node based on resources. In the second module, the best optimal nodes are selected as a head node by applying the genetic operations such as crossover, mutation, and fitness function by considering the available resources. In the selected optimal nodes, one node is chosen as a head node and the other is considered as a candidate node. In the third module, the candidate node becomes the head node in the case of head node failure. The proposed method HNSA is compared against the state-of-the-art algorithms such as Bees Life Algorithm (BLA) and Heterogeneous Earliest Finished Time (HEFT). The simulation analysis shows that the proposed HNSA technique performs better in terms of execution time, memory utilization, service level sgreement (SLA) violation, and energy consumption. [ABSTRACT FROM AUTHOR]

In order to improve reliability and fatigue life of cylinder gaskets in heavy duty diesel engine, several methods and algorithms are applied to optimize operating factors of gaskets. Finite element method is utilized to figure out and analyze the temperature fields, thermal-mechanical coupling stress fields, and deformations of gasket. After determining the maximum values of three state parameters, the orthogonal experimental design method is adopted to analyze the influence rules of five operating factors on three state parameters of the gaskets and four factors which most significantly affect these state parameters are determined. Then, the method which uses operating factors to predict state parameters is established on the application of hybrid neuron network based on partial least squares regression and deep neural network. The comparison results between the predicted values and real values verified the accuracy of the hybrid neuron network method. Based on artificial bee colony algorithm, improvement is attached to the way three kinds of grey wolves locate preys in grey wolf algorithm and the way how using different hierarchy wolfs in grey wolf algorithm to determine three weight coefficients and the location of prey is put forward with. The method using artificial bee colony algorithm to optimize the grey wolf algorithm is called ABC and GWO. The proposed HNN and the ABC and GWO method are applied to work out operating factors values which correspond to optimal state parameters of gasket, and the gaskets are optimized according to the optimal values. It has been demonstrated by finite element analysis results that maximum temperature, maximum coupling stress, and the maximum deformation decrease to 6 K, 12.57 MPa, and 0.0925 mm compared to the original values, respectively, which proves the accuracy of the algorithm and the validity of the improvement. [ABSTRACT FROM AUTHOR]

Viscoelastic artificial boundary elements are one of the most commonly used artificial boundaries when solving dynamic soil-structure interactions or near-field wave propagation problems. However, due to the lack of clear and practical stability criteria for the explicit algorithm that considers the influence of viscoelastic artificial boundary elements, the determination of the stable time increment in such numerical analyses is still a challenge. In this study, we proposed a numerical stability analysis method for the explicit algorithm with a 3D viscoelastic artificial boundary element based on the idea of a subsystem. Through this method, the artificial boundary subsystem that controls the stability of the overall numerical system is determined, and the analytical solution for the stability condition of the explicit integration algorithm with 3D viscoelastic artificial boundary elements is obtained. On this basis, the maximum time increment for solving dynamic problems with viscoelastic artificial boundary elements can be determined. [ABSTRACT FROM AUTHOR]