Multi-objective optimization Control and Data Analysis of Intersection Considering Vehicle Exhaust Emission

2020 ◽  
Author(s):  
Zijun Liang ◽  
Yun Xiao ◽  
Yuedong Zhao ◽  
Huanxiao Liu
Keyword(s):  
Data Analysis ◽  
Exhaust Emission ◽  
Multi Objective Optimization ◽  
Vehicle Exhaust ◽  
Multi Objective ◽  
Optimization Control

Clusters

2017 ◽  
pp. 179-190
Keyword(s):  
Cluster Analysis ◽  
Data Analysis ◽  
Statistical Data ◽  
Similarity Measures ◽  
Trial And Error ◽  
Multi Objective Optimization ◽  
Statistical Data Analysis ◽  
Multi Objective ◽  
Common Technique

Cluster analysis, which we approach in this chapter, is the task of grouping a set of objects in such a way that objects in the same group or cluster are more similar to each other than to those in other groups or clusters. It is a common technique for statistical data analysis. Cluster analysis can be achieved by various algorithms that might differ significantly. Therefore, cluster analysis as such is not a trivial task. It is an interactive multi-objective optimization that involves trial and error. Therefore, in cluster analysis, the clustering of subjects or variables are made from similarity measures or dissimilarity (distance) between two subjects initially, and later between two clusters. These groups can be done using hierarchical or non-hierarchical techniques.

Multi-objective optimization control method for multi-user multi-service access in heterogeneous wireless networks

2019 ◽  
Vol 34 (7) ◽  
pp. 708-715
Author(s):  
董晓庆 DONG Xiao-qing ◽  
程良伦 CHENG Liang-lun ◽  
陈洪财 CHEN Hong-cai ◽  
郑耿忠 ZHENG Geng-zhong ◽  
谢森林 XIE Sen-lin
Keyword(s):  
Wireless Networks ◽  
Control Method ◽  
Heterogeneous Wireless Networks ◽  
Service Access ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Control

Determination of Multi-Objective Optimization Control Variable Domain Value in Coordinated Control System of Coal-Fired Unit

2011 ◽  
Vol 282-283 ◽  
pp. 726-730
Author(s):  
Jun Jie Gu ◽  
Zhi Yang ◽  
Yan Ling Ren
Keyword(s):  
Control System ◽  
Fitness Function ◽  
Control Variable ◽  
Physical Significance ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Control Value ◽  
Optimization Control ◽  
System Variables

The accuracy of the variables variation scope in fitness function of the multi-objective optimization have an important influence to multi-objective optimization results. Take a 300 MW coal-fired unit as an example, according to the system mechanism builds a boiler-turbine dynamic model. And put forward a method, in this paper, which is using the iteration way and observing its physical significance to determine control system variables scope. The simplified model uses fuel value, turbine value and feedwater value as the inputs, and uses power, feedwater flow and absorbed heat of water wall as the outputs, to get the boundary of the pressure and the control value of the inputs during 50%-100% load.

scholarly journals Multi-objective optimization and data analysis in informationization

Computing ◽  
2019 ◽  
Vol 101 (6) ◽  
pp. 495-498 ◽  
Author(s):  
Kelvin Kian Loong Wong ◽  
Zhihua Liu ◽  
Quan Zou
Keyword(s):  
Data Analysis ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Multi-Objective Optimization Benchmarking Using DSCTool

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 839 ◽  
Author(s):  
Peter Korošec ◽  
Tome Eftimov
Keyword(s):  
Data Analysis ◽  
Statistical Analysis ◽  
Research Area ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Level Statistics ◽  
E Learning ◽  
Exploratory Data ◽  
Using Data

By performing data analysis, statistical approaches are highly welcome to explore the data. Nowadays with the increases in computational power and the availability of big data in different domains, it is not enough to perform exploratory data analysis (descriptive statistics) to obtain some prior insights from the data, but it is a requirement to apply higher-level statistics that also require much greater knowledge from the user to properly apply them. One research area where proper usage of statistics is important is multi-objective optimization, where the performance of a newly developed algorithm should be compared with the performances of state-of-the-art algorithms. In multi-objective optimization, we are dealing with two or more usually conflicting objectives, which result in high dimensional data that needs to be analyzed. In this paper, we present a web-service-based e-Learning tool called DSCTool that can be used for performing a proper statistical analysis for multi-objective optimization. The tool does not require any special statistics knowledge from the user. Its usage and the influence of a proper statistical analysis is shown using data taken from a benchmarking study performed at the 2018 IEEE CEC (The IEEE Congress on Evolutionary Computation) is appropriate. Competition on Evolutionary Many-Objective Optimization.

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