scholarly journals An Optimal Allocation Method of Power Multimodal Network Resources Based on NSGA-II

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ao Xiong ◽  
Yuanzheng Tong ◽  
Shaoyong Guo ◽  
Yanru Wang ◽  
Sujie Shao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Optimal Allocation ◽  
Optimization Method ◽  
Resource Optimization ◽  
Normal Operation ◽  
Network Resources ◽  
Nsga Ii ◽  
Network Resource ◽  
Allocation Method ◽  
Multimodal Network

Basic services for power business were provided by the power multimodel network providers. However, because the power multimodal network is usually complex and changeable, the service of power business is often unstable. This problem can be solved by a suitable network resource optimization method. Therefore, how to design a network resource optimization method that seeks a compromise between multiple performance indicators that achieve the normal operation of power multimode networks is still extremely challenging. An optimal allocation method of power multimodal network resources based on NSGA-II was proposed by this paper. Firstly, the power multimodal network-resource model is established, and the problems existing in the resource optimization process are analyzed. Secondly, preprocessing technology and indirect coding technology are applied to NSGA-II, which solves the coding problem and convergence problem of the application of genetic algorithm to the optimization of network resource allocation. Finally, the simulation results show that, compared with the control algorithm, this method has further optimized the various indicators of the resource allocation of the power multimodal network, and the performance has been improved by more than 6%.

scholarly journals Role of Machine Learning in Resource Allocation Strategy over Vehicular Networks: A Survey

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6542
Author(s):  
Ida Nurcahyani ◽  
Jeong Woo Lee
Keyword(s):  
Machine Learning ◽  
Resource Allocation ◽  
Vehicular Networks ◽  
Learning Algorithms ◽  
Vehicular Network ◽  
Machine Learning Algorithms ◽  
Allocation Strategy ◽  
Network Resources ◽  
Network Resource ◽  
Resource Allocation Strategy

The increasing demand for smart vehicles with many sensing capabilities will escalate data traffic in vehicular networks. Meanwhile, available network resources are limited. The emergence of AI implementation in vehicular network resource allocation opens the opportunity to improve resource utilization to provide more reliable services. Accordingly, many resource allocation schemes with various machine learning algorithms have been proposed to dynamically manage and allocate network resources. This survey paper presents how machine learning is leveraged in the vehicular network resource allocation strategy. We focus our study on determining its role in the mechanism. First, we provide an analysis of how authors designed their scenarios to orchestrate the resource allocation strategy. Secondly, we classify the mechanisms based on the parameters they chose when designing the algorithms. Finally, we analyze the challenges in designing a resource allocation strategy in vehicular networks using machine learning. Therefore, a thorough understanding of how machine learning algorithms are utilized to offer a dynamic resource allocation in vehicular networks is provided in this study.

Application of BP Neural Network to Optimize the Allocation of Art Teaching Resources

2021 ◽  
Vol 7 (5) ◽  
pp. 4122-4132
Author(s):  
Xu Yingjie
Keyword(s):  
Resource Allocation ◽  
Optimization Problem ◽  
Optimal Allocation ◽  
Transmission Rate ◽  
Optimal Solution ◽  
Delay Model ◽  
Network Resource ◽  
Teaching Resources ◽  
Art Teaching ◽  
Network Resource Allocation

Reasonable allocation of art teaching resources can improve the management efficiency of art teaching resources. There is a large delay in the allocation of art teaching resources, which leads to the long occupation time of network resource allocation channel. The traditional method of network experiment resource allocation is to assign resource tasks for different channels to complete the resource allocation. When the network resource allocation channel occupies a long time, the allocation efficiency is reduced. This paper proposes an optimal allocation method of art teaching resources based on multi rate cognition. From the point of view that there are a pair of primary users and a pair of secondary users in the network, this method constructs a resource allocation delay model, obtains the resource allocation delay under different modes, and dynamically adjusts the transmission rate on the allocation resource block. The art teaching resource allocation scheduling problem is modeled as a nonlinear optimization problem, and the constraints of the optimization problem are given, which are integrated into greedy computing. The global optimal solution of the problem is carried out by using the method, and the allocation of art teaching resources is completed. Simulation results show that the proposed algorithm greatly improves the efficiency and effect of teaching network resource allocation.

scholarly journals A Heuristic Algorithm for Solving Triangle Packing Problem

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Ruimin Wang ◽  
Yuqiang Luo ◽  
Jianqiang Dong ◽  
Shuai Liu ◽  
Xiaozhuo Qi
Keyword(s):  
Approximate Solution ◽  
Time Complexity ◽  
Complexity Analysis ◽  
Packing Problem ◽  
Optimization Method ◽  
Resource Optimization ◽  
Network Resource ◽  
Analogue Experiment ◽  
Processing Network ◽  
Application Prospects

The research on the triangle packing problem has important theoretic significance, which has broad application prospects in material processing, network resource optimization, and so forth. Generally speaking, the orientation of the triangle should be limited in advance, since the triangle packing problem is NP-hard and has continuous properties. For example, the polygon is not allowed to rotate; then, the approximate solution can be obtained by optimization method. This paper studies the triangle packing problem by a new kind of method. Such concepts as angle region, corner-occupying action, corner-occupying strategy, and edge-conjoining strategy are presented in this paper. In addition, an edge-conjoining and corner-occupying algorithm is designed, which is to obtain an approximate solution. It is demonstrated that the proposed algorithm is highly efficient, and by the time complexity analysis and the analogue experiment result is found.

An Optimization Method for Geometric Error of Machine Tool Based on NSGA-II

2013 ◽  
Vol 418 ◽  
pp. 180-186
Author(s):  
Li Gang Cai ◽  
Cui Zhang ◽  
Qiang Cheng ◽  
Pei Hua Gu ◽  
Hong Ying Wang
Keyword(s):  
Machine Tool ◽  
Optimization Method ◽  
Geometric Error ◽  
Machining Accuracy ◽  
Body System ◽  
Critical Problem ◽  
Nsga Ii ◽  
Allocation Method ◽  
The Cost ◽  
Multi Body

Balancing the cost and processing precision of machine tool by the method of error allocation without affecting the machining performances is a critical problem in the Machine tool industry. In this paper, a new accuracy allocation method for multi-axis machine tool based on Multi-body system theory, manufacturing and quality loss costs and relationship between tolerances and accuracy parameters of components is proposed. This optimization method is performed with Non-Dominated Sorting Genetic Algorithm II algorithm using Isight and Matlab software. A three-axis vertical machine tool is taken as an example to demonstrate the method, and the optimization results show that the accuracy allocation method proposed is feasible in the optimization of geometric errors on the premise of satisfying machining accuracy requirements.

scholarly journals Multi-objective optimal allocation of water resources based on ‘three red lines’ in Qinzhou, China

Water Policy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 541-560
Author(s):  
Haopeng Guan ◽  
Lihua Chen ◽  
Shuping Huang ◽  
Cheng Yan ◽  
Yan Wang
Keyword(s):  
Resource Allocation ◽  
Water Resources ◽  
Water Resource ◽  
Optimal Allocation ◽  
Supply And Demand ◽  
Domestic Water ◽  
Nsga Ii ◽  
Allocation Model ◽  
Water Resource Allocation ◽  
Multi Objective

Abstract Water shortages and pollution emerge because of anthropogenic demands. Since 2011, ‘China's Most Stringent Water Resources Management’ (CMSWRM) has been comprehensively enacted in the country. This paper presents the characteristics of the ‘three red lines’ (TRL) and a multi-objective optimal allocation model based on the TRL constraint, considering the benefits for society, the economy, and the environment. This model had been applied to the reasonable allocation of water supply and demand in Qinzhou for the planning years of 2020 and 2030. Two water resource allocation scenarios for these years were configured by setting different chemical oxygen demand (COD) concentrations for wastewater discharge in the municipal, secondary, tertiary, and agricultural sectors. The gamultiobj function based on the NSGA-II algorithm was used to solve the model in MATLAB. The results indicate that if COD concentrations in each sector are not reduced, then restrictions on domestic water sources will be necessary, both in 2020 and 2030. The two water resource allocation scenarios in 2020 and 2030 can provide a reference for decision-makers in Qinzhou to implement CMSWRM.

scholarly journals A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jing Chen ◽  
Yinglong Wang ◽  
Tao Liu
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Evolutionary Algorithm ◽  
Optimization Model ◽  
Prediction Method ◽  
Nsga Ii ◽  
Adaptive Prediction ◽  
Allocation Method ◽  
Elite Strategy ◽  
Complex Features

AbstractWith the development of big data and artificial intelligence, cloud resource requests present more complex features, such as being sudden, arriving in batches and being diverse, which cause the resource allocation to lag far behind the resource requests and an unbalanced resource utilization that wastes resources. To solve this issue, this paper proposes a proactive resource allocation method based on the adaptive prediction of the resource requests in cloud computing. Specifically, this method first proposes an adaptive prediction method based on the runs test that improves the prediction accuracy of resource requests, and then, it builds a multiobjective resource allocation optimization model, which alleviates the latency of the resource allocation and balances the utilizations of the different types of resources of a physical machine. Furthermore, a multiobjective evolutionary algorithm, the Nondominated Sorting Genetic Algorithm with the Elite Strategy (NSGA-II), is improved to further reduce the resource allocation time by accelerating the solution speed of the multiobjective optimization model. The experimental results show that this method realizes the balanced utilization between the CPU and memory resources and reduces the resource allocation time by at least 43% (10 threads) compared with the Improved Strength Pareto Evolutionary algorithm (SPEA2) and NSGA-II methods.

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