scholarly journals Energy Consumption Estimation of the Electric Bus Based on Grey Wolf Optimization Algorithm and Support Vector Machine Regression

2021 ◽  
Vol 13 (9) ◽  
pp. 4689
Author(s):  
Wei Qin ◽  
Linhong Wang ◽  
Yuhan Liu ◽  
Cheng Xu
Keyword(s):  
Support Vector Machine ◽  
Energy Consumption ◽  
Optimization Algorithm ◽  
Support Vector ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Support Vector Machine Regression ◽  
Electric Bus ◽  
Proposed Model ◽  
The Mean

Electric buses have many significant advantages, such as zero emissions and low noise and energy consumption, making them play an important role in saving the operation cost of bus companies and reducing urban traffic pollution emissions. Therefore, in recent years, many cities in the world dedicate to promoting the electrification of public transport vehicles. Whereas due to the limitation of on-board battery capacity, the driving range of electric buses is relatively short. The accurate estimation of energy consumption on the electric bus routes is the premise of conducting bus scheduling and optimizing the layout of charging facilities. This study collected the actual operation data of three electric bus routes in Meihekou City, China, and established the support vector machine regression (SVR) model by taking the state of charge (SOC), trip travel time, mean environment temperature and air-conditioning operation time as the independent variables; while the energy consumptions of the route operations served as the dependent variables. Furthermore, the grey wolf optimization (GWO) algorithm was adopted to select the optimal parameters of the proposed model. Finally, a support vector machine regression model based on the grey wolf optimization algorithm (GWO-SVR) is proposed. Three real bus lines were taken as examples to validate the model. The results show that the mean average percentage error is 14.47% and the mean average error is 0.7776. In addition, the estimation accuracy and training time of the proposed model are superior to the genetic algorithm-back propagation neural network model and grid-search support vector machine regression model.

Fog Enabled Cloud Based Intelligent Resource Management Approach Using Improved Grey Wolf Optimization Strategy and Kernel Support Vector Machine

2021 ◽  
Vol 18 (4) ◽  
pp. 1275-1281
Author(s):  
R. Sudha ◽  
G. Indirani ◽  
S. Selvamuthukumaran
Keyword(s):  
Support Vector Machine ◽  
Resource Management ◽  
Support Vector ◽  
Management Approach ◽  
Optimization Strategy ◽  
Grey Wolf ◽  
Processing Load ◽  
Grey Wolf Optimization ◽  
Kernel Support Vector Machine ◽  
Service Response Time

Resource management is a significant task of scheduling and allocating resources to applications to meet the required Quality of Service (QoS) limitations by the minimization of overhead with an effective resource utilization. This paper presents a Fog-enabled Cloud computing resource management model for smart homes by the Improved Grey Wolf Optimization Strategy. Besides, Kernel Support Vector Machine (KSVM) model is applied for series forecasting of time and also of processing load of a distributed server and determine the proper resources which should be allocated for the optimization of the service response time. The presented IGWO-KSVM model has been simulated under several aspects and the outcome exhibited the outstanding performance of the presented model.

scholarly journals Forecasting of Commodity Future Index using a Hybrid Regression Model based on Support Vector Machine and Grey Wolf Optimization Algorithm

2019 ◽  
Vol 9 (2) ◽  
pp. 2856-2862
Keyword(s):  
Support Vector Machine ◽  
Time Series Data ◽  
Optimization Problems ◽  
Series Data ◽  
Support Vector ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
The Everyday ◽  
Hybrid Forecasting ◽  
Commodity Future

The forecasting and investigation of finance time series data are hard, and are the most confounded works pertained with investor decision. In this paper, an economic derivative instrument for Multi Commodity Exchange (MCX) index of CRUDEOIL is estimated by utilizing forecasting models based on recently formulated artificial intelligence (AI) approaches. These approaches have been appeared to perform astoundingly well in different optimization problems. Specifically, a novel hybrid forecasting model is designed by combining the support vector machine (SVM) and grey wolf optimization (GWO) and it is named as hybrid SVM-GWO. The presented hybrid SVM-GWO model eliminates the user determined control parameter, which is needed for other AI techniques. The practicality and proficiency of the presented SVM-GWO regression method is evaluated by predicting the everyday close price of CRUDEOIL index traded in the MCX of India Limited. The exploratory outcomes depicts that the present hybrid SVM-GWO technique is viable and outperforms superior to the conventional SVM, hybrid SVM-TLBO and SVM-PSO regression models

scholarly journals A Two-Stage Wireless Sensor Grey Wolf Optimization Node Location Algorithm Based on K-Value Collinearity

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yinghui Meng ◽  
Qianying Zhi ◽  
Qiuwen Zhang ◽  
Erlin Lin
Keyword(s):  
Energy Consumption ◽  
Optimization Algorithm ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Two Stage ◽  
Essential Information ◽  
K Value ◽  
Initial Location

In the practical application of WSN (wireless sensor network), location information of the sensor nodes has become one of the essential information pieces in the whole network. At present, some localization algorithms use intelligent optimization algorithm to optimize the node group directly. Although the overall localization error is reduced, the location deviation of individual unknown nodes will be larger, and the large number of iterations will cause a large energy consumption of nodes. Aiming at the above problems, this paper comes up with a two-stage WSN localization algorithm based on the degree of K-value collinearity (DC-K) and improved grey wolf optimization. The first stage is aiming at the defects of the existing collinearity algorithm, putting forward the concept of DC-K, according to the K-value to carry out the initial location in the first stage. The second stage is using the improved grey wolf optimization algorithm to optimize the location results which were obtained in the first stage, so as to get more accurate location results. The experimental results display that this localization algorithm with a better localization accuracy has high robustness and has fewer iterations in the optimization process, which greatly reduces the energy consumption of nodes.

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