Research on the Station Layout Method of Ground-Based Pseudolite Positioning System Based on NSGA-II Algorithm

Given the problem that the existing method of station distributing the pseudosatellite system cannot ensure both its coverage and position in a situation of signal occlusion, it proposed a new stationary layout method with an elite strategy for a ground-based pseudosatellite positioning system based on the elite strategy of the nondominant genetic rankings (NSGA-II). The geometrical design of the pseudosatellite system is calculated by visual domain analysis and precision factors for the signal coverage age and base station. To optimize the algorithm, the NSGA-II algorithm is used. An earth pseudosatellite positioning system method of stationary distribution is obtained that simultaneously optimizes signal coverage and positioning accuracy. The algorithm is better distributed and has a certain superintendence compared with the traditional genetic algorithm.

Download Full-text

A fusion optimization algorithm of network element layout for indoor positioning

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-019-1597-8 ◽

2019 ◽

Vol 2019 (1) ◽

Author(s):

Xiao-min Yu ◽

Hui-qiang Wang ◽

Hong-wu Lv ◽

Xiu-bing Liu ◽

Jin-qiu Wu

Keyword(s):

Genetic Algorithm ◽

Optimization Algorithm ◽

Ant Colony Algorithm ◽

Three Dimensional ◽

Indoor Positioning ◽

Base Station ◽

Adaptive Genetic Algorithm ◽

Network Element ◽

Positioning Accuracy ◽

Ga Algorithm

AbstractThe indoor scene has the characteristics of complexity and Non-Line of Sight (NLOS). Therefore, in the application of cellular network positioning, the layout of the base station has a significant influence on the positioning accuracy. In three-dimensional indoor positioning, the layout of the base station only focuses on the network capacity and the quality of positioning signal. At present, the influence of the coverage and positioning accuracy has not been considered. Therefore, a network element layout optimization algorithm based on improved Adaptive Simulated Annealing and Genetic Algorithm (ASA-GA) is proposed in this paper. Firstly, a three-dimensional positioning signal coverage model and a base station layout model are established. Then, the ASA-GA algorithm is proposed for optimizing the base station layout scheme. Experimental results show that the proposed ASA-GA algorithm has a faster convergence speed, which is 16.7% higher than the AG-AC (Adaptive Genetic Combining Ant Colony) algorithm. It takes about 25 generations to achieve full coverage. At the same time, the proposed algorithm has better coverage capability. After optimization of the layout of the network element, the effective coverage rate is increased from 89.77 to 100% and the average location error decreased from 2.874 to 0.983 m, which is about 16% lower than the AG-AC algorithm and 22% lower than the AGA (Adaptive Genetic Algorithm) algorithm.

Download Full-text

Multi-UAV Mission Allocation under Constraint

Applied Sciences ◽

10.3390/app9112184 ◽

2019 ◽

Vol 9 (11) ◽

pp. 2184 ◽

Cited By ~ 8

Author(s):

Songyan Xie ◽

An Zhang ◽

Wenhao Bi ◽

Yongchuan Tang

Keyword(s):

Genetic Algorithm ◽

Mathematical Model ◽

Time Window ◽

Reduction Process ◽

Target Area ◽

Nsga Ii ◽

Elite Strategy ◽

Strategic Value ◽

The Difference ◽

Improved Model

This paper is devoted to the unmanned aerial vehicle (UAV) mission allocation problem. To solve this problem in a more realistic battlefield environment, an improved mathematical model for UAV mission allocation is proposed. Being different from previous formulations, this model not only considers the difference in the importance of the target but also the constraints of the time window. In addition, an indicator of reconnaissance reward is added to this model. Each target area has a different importance, just as the strategic value of each region is different in combat. In this paper, we randomly generate the value factor for each reconnaissance area. To solve the mathematical model with different operational intentions, a dimensionality reduction process for which the reconnaissance reward is the optimization objective is presented. Finally, based on the improved model, the simulation result with Lingo is compared with that of non-dominated sorting genetic algorithm with elite strategy (NSGA-II) and genetic algorithm (GA) to verify the reliability and the effectiveness of the improved method.

Download Full-text

Optimization of Shear Wave Velocity (Vs) from a Post-Liquefaction Settlement Using a Genetic Algorithm Multi-Objective NSGA II

International Review of Mechanical Engineering (IREME) ◽

10.15866/ireme.v11i3.11226 ◽

2017 ◽

Vol 11 (3) ◽

pp. 175 ◽

Cited By ~ 1

Author(s):

Kamel Goudjil ◽

Badreddine Sbartai

Keyword(s):

Genetic Algorithm ◽

Shear Wave ◽

Wave Velocity ◽

Shear Wave Velocity ◽

Nsga Ii ◽

Multi Objective

Download Full-text

Dynamic and Positioning Performances of Linear Electromechanical Actuator

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.809-810.682 ◽

2015 ◽

Vol 809-810 ◽

pp. 682-687

Author(s):

Vasile Nasui ◽

Mihai Banica ◽

Dinu Darabă

Keyword(s):

Dynamic Characteristics ◽

Practical Importance ◽

Correct Identification ◽

Positioning System ◽

Positioning Error ◽

Servo Motor ◽

Positioning Accuracy ◽

Electromechanical Actuator ◽

Initial Errors ◽

Selection For

This paper presents the dynamic characteristics and the proposed positioning performance of the system to them investigated experimentally. In this research, we developed the positioning system and we evaluated positioning accuracy. The developed system uses a servo motor for motion actuation. In this paper, we focused on studying the dependency of the positioning error – elementary errors – the position of the conducting element for the mechanism of the transformation of the rotation translation movement, representatively the mechanism screw – screwdriver and on emphasizing the practical consequences in the field of design, regulation and exploitation of the correct identification of all the initial errors in the structure of the mechanism, their character and the selection for an ultimate calculus of these which are of a real practical importance.

Download Full-text

Assessing a Multi-Objective Genetic Algorithm with a Simulated Environment for Energy-Saving of Air Conditioning Systems with User Preferences

Symmetry ◽

10.3390/sym13020344 ◽

2021 ◽

Vol 13 (2) ◽

pp. 344

Author(s):

Alejandro Humberto García Ruiz ◽

Salvador Ibarra Martínez ◽

José Antonio Castán Rocha ◽

Jesús David Terán Villanueva ◽

Julio Laria Menchaca ◽

...

Keyword(s):

Genetic Algorithm ◽

Energy Consumption ◽

Energy Saving ◽

User Satisfaction ◽

Air Conditioning ◽

State Of The Art ◽

User Preferences ◽

The State ◽

Nsga Ii ◽

Air Conditioning System

Electricity is one of the most important resources for the growth and sustainability of the population. This paper assesses the energy consumption and user satisfaction of a simulated air conditioning system controlled with two different optimization algorithms. The algorithms are a genetic algorithm (GA), implemented from the state of the art, and a non-dominated sorting genetic algorithm II (NSGA II) proposed in this paper; these algorithms control an air conditioning system considering user preferences. It is worth noting that we made several modifications to the objective function’s definition to make it more robust. The energy-saving optimization is essential to reduce CO2 emissions and economic costs; on the other hand, it is desirable for the user to feel comfortable, yet it will entail a higher energy consumption. Thus, we integrate user preferences with energy-saving on a single weighted function and a Pareto bi-objective problem to increase user satisfaction and decrease electrical energy consumption. To assess the experimentation, we constructed a simulator by training a backpropagation neural network with real data from a laboratory’s air conditioning system. According to the results, we conclude that NSGA II provides better results than the state of the art (GA) regarding user preferences and energy-saving.

Download Full-text

Analysis of the Applicability of Dilution of Precision in the Base Station Configuration Optimization of Ultrawideband Indoor TDOA Positioning System

IEEE Access ◽

10.1109/access.2020.3045189 ◽

2020 ◽

pp. 1-1

Author(s):

Mei Wang ◽

Zhao Chen ◽

Zou Zhou ◽

Jielin Fu ◽

Hongbing Qiu

Keyword(s):

Base Station ◽

Positioning System ◽

Configuration Optimization ◽

Dilution Of Precision

Download Full-text

The integrated optimization of underground stope layout designing and production scheduling incorporating a non-dominated sorting genetic algorithm (NSGA-II)

Resources Policy ◽

10.1016/j.resourpol.2019.101408 ◽

2019 ◽

Vol 63 ◽

pp. 101408 ◽

Cited By ~ 6

Author(s):

Sorayya Foroughi ◽

Jafar Khademi Hamidi ◽

Masoud Monjezi ◽

Micah Nehring

Keyword(s):

Genetic Algorithm ◽

Production Scheduling ◽

Nsga Ii ◽

Integrated Optimization

Download Full-text

Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression

Applied Sciences ◽

10.3390/app9061048 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1048 ◽

Cited By ~ 8

Author(s):

Huy Tran ◽

Cheolkeun Ha

Keyword(s):

Machine Learning ◽

Visible Light ◽

Noise Reduction ◽

Indoor Positioning ◽

Computational Time ◽

Dual Function ◽

Positioning System ◽

Positioning Accuracy ◽

Positioning Systems ◽

Machine Learning Classification

Recently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor visible light positioning system and dual-function machine learning (ML) algorithms. Our solution increases positioning accuracy under the negative effect of multipath reflections and decreases the computational time for ML algorithms. Initially, we perform a noise reduction process to eliminate low-intensity reflective signals and minimize noise. Then, we divide the floor of the room into two separate areas using the ML classification function. This significantly reduces the computational time and partially improves the positioning accuracy of our system. Finally, the regression function of those ML algorithms is applied to predict the location of the optical receiver. By using extensive computer simulations, we have demonstrated that the execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied. In the best case, the proposed solution took 78.26% less time and provided a 52.55% improvement in positioning accuracy.

Download Full-text

Carbon Emission Reduction of Apparel Material Distribution Based on Multi-Objective Genetic Algorithm (NSGA-II)

Sustainability ◽

10.3390/su11092571 ◽

2019 ◽

Vol 11 (9) ◽

pp. 2571

Author(s):

Xujing Zhang ◽

Lichuan Wang ◽

Yan Chen

Keyword(s):

Genetic Algorithm ◽

Carbon Emissions ◽

Optimal Solution ◽

Material Distribution ◽

Low Carbon ◽

Nsga Ii ◽

Management Objectives ◽

Multi Objective Genetic Algorithm ◽

Distribution Process ◽

The Cost

Low-carbon production has become one of the top management objectives for every industry. In garment manufacturing, the material distribution process always generates high carbon emissions. In order to reduce carbon emissions and the number of operators to meet enterprises’ requirements to control the cost of production and protect the environment, the paths of material distribution were analyzed to find the optimal solution. In this paper, the model of material distribution to obtain minimum carbon emissions and vehicles (operators) was established to optimize the multi-target management in three different production lines (multi-line, U-shape two-line, and U-shape three-line), while the workstations were organized in three ways: in the order of processes, in the type of machines, and in the components of garment. The NSGA-II algorithm (non-dominated sorting genetic algorithm-II) was applied to obtain the results of this model. The feasibility of the model and algorithm was verified by the practice of men’s shirts manufacture. It could be found that material distribution of multi-line layout produced the least carbon emissions when the machines were arranged in the group of type.

Download Full-text