Using the Decomposition-Based Multi-Objective Evolutionary Algorithm with Adaptive Neighborhood Sizes and Dynamic Constraint Strategies to Retrieve Atmospheric Ducts

Using the Decomposition-Based Multi-Objective Evolutionary Algorithm with Adaptive Neighborhood Sizes and Dynamic Constraint Strategies to Retrieve Atmospheric Ducts

The traditional method of retrieving atmospheric ducts is to use the special sensor of weather balloons or rocket soundings to obtain information intelligently, and it is very expensive. Today, with the development of technology, it is very convenient to retrieve the atmospheric ducts from Global Na...

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Main Authors: Yanbo Mai, Hanqing Shi, Qixiang Liao, Zheng Sheng, Shuai Zhao, Qingjian Ni, Wei Zhang
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Sensors
Subjects:
special sensor
GNSS
new algorithm
balance the diversity and convergence of the population
atmospheric ducts
Online Access:https://www.mdpi.com/1424-8220/20/8/2230
id doaj-44e20913d7b840aeaa1c358725fdb8e1
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spelling doaj-44e20913d7b840aeaa1c358725fdb8e12020-11-25T02:01:13ZengMDPI AGSensors1424-82202020-04-01202230223010.3390/s20082230Using the Decomposition-Based Multi-Objective Evolutionary Algorithm with Adaptive Neighborhood Sizes and Dynamic Constraint Strategies to Retrieve Atmospheric DuctsYanbo Mai0Hanqing Shi1Qixiang Liao2Zheng Sheng3Shuai Zhao4Qingjian Ni5Wei Zhang6College of Meteorology and Oceanography, National University of Defense Technology, Nanjing 210000, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Nanjing 210000, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Nanjing 210000, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Nanjing 210000, ChinaSchool of Computer Science and Engineering, Southeast University, Nanjing 210000, ChinaSchool of Computer Science and Engineering, Southeast University, Nanjing 210000, ChinaCollege of Meteorology and Oceanography, National University of Defense Technology, Nanjing 210000, ChinaThe traditional method of retrieving atmospheric ducts is to use the special sensor of weather balloons or rocket soundings to obtain information intelligently, and it is very expensive. Today, with the development of technology, it is very convenient to retrieve the atmospheric ducts from Global Navigation Satellite System (GNSS) phase delay and propagation loss observation data, and then the GNSS receiver on the ground forms an automatic receiving sensor. This paper proposes a hybrid decomposition-based multi-objective evolutionary algorithm with adaptive neighborhood sizes (EN-MOEA/ACD-NS), which dynamically imposes some constraints on the objectives. The decomposition-based multi-objective evolutionary algorithm (MOEA/D) updates the solutions through neighboring objectives, the number of which affects the quality of the optimal solution. Properly constraining the optimization objectives can effectively balance the diversity and convergence of the population. The experimental results from the Congress on Evolutionary Computation (CEC) 2009 on test instances with hypervolume (HV), inverted generational distance (IGD), and average Hausdorff distance ∆<sub>2</sub> metrics show that the new method performs similarly to the evolutionary algorithm MOEA/ACD-NS, which considers only the dynamic change of the neighborhood sizes. The improved algorithm is applied to the practical problem of jointly retrieving atmospheric ducts with GNSS signals, and its performance further demonstrates its feasibility and practicability.https://www.mdpi.com/1424-8220/20/8/2230special sensorGNSSnew algorithmbalance the diversity and convergence of the populationatmospheric ducts
collection DOAJ
language English
format Article
sources DOAJ
author Yanbo Mai
Hanqing Shi
Qixiang Liao
Zheng Sheng
Shuai Zhao
Qingjian Ni
Wei Zhang
spellingShingle Yanbo Mai
Hanqing Shi
Qixiang Liao
Zheng Sheng
Shuai Zhao
Qingjian Ni
Wei Zhang
Using the Decomposition-Based Multi-Objective Evolutionary Algorithm with Adaptive Neighborhood Sizes and Dynamic Constraint Strategies to Retrieve Atmospheric Ducts
Sensors
special sensor
GNSS
new algorithm
balance the diversity and convergence of the population
atmospheric ducts
author_facet Yanbo Mai
Hanqing Shi
Qixiang Liao
Zheng Sheng
Shuai Zhao
Qingjian Ni
Wei Zhang
author_sort Yanbo Mai
title Using the Decomposition-Based Multi-Objective Evolutionary Algorithm with Adaptive Neighborhood Sizes and Dynamic Constraint Strategies to Retrieve Atmospheric Ducts
title_short Using the Decomposition-Based Multi-Objective Evolutionary Algorithm with Adaptive Neighborhood Sizes and Dynamic Constraint Strategies to Retrieve Atmospheric Ducts
title_full Using the Decomposition-Based Multi-Objective Evolutionary Algorithm with Adaptive Neighborhood Sizes and Dynamic Constraint Strategies to Retrieve Atmospheric Ducts
title_fullStr Using the Decomposition-Based Multi-Objective Evolutionary Algorithm with Adaptive Neighborhood Sizes and Dynamic Constraint Strategies to Retrieve Atmospheric Ducts
title_full_unstemmed Using the Decomposition-Based Multi-Objective Evolutionary Algorithm with Adaptive Neighborhood Sizes and Dynamic Constraint Strategies to Retrieve Atmospheric Ducts
title_sort using the decomposition-based multi-objective evolutionary algorithm with adaptive neighborhood sizes and dynamic constraint strategies to retrieve atmospheric ducts
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2020-04-01
description The traditional method of retrieving atmospheric ducts is to use the special sensor of weather balloons or rocket soundings to obtain information intelligently, and it is very expensive. Today, with the development of technology, it is very convenient to retrieve the atmospheric ducts from Global Navigation Satellite System (GNSS) phase delay and propagation loss observation data, and then the GNSS receiver on the ground forms an automatic receiving sensor. This paper proposes a hybrid decomposition-based multi-objective evolutionary algorithm with adaptive neighborhood sizes (EN-MOEA/ACD-NS), which dynamically imposes some constraints on the objectives. The decomposition-based multi-objective evolutionary algorithm (MOEA/D) updates the solutions through neighboring objectives, the number of which affects the quality of the optimal solution. Properly constraining the optimization objectives can effectively balance the diversity and convergence of the population. The experimental results from the Congress on Evolutionary Computation (CEC) 2009 on test instances with hypervolume (HV), inverted generational distance (IGD), and average Hausdorff distance ∆<sub>2</sub> metrics show that the new method performs similarly to the evolutionary algorithm MOEA/ACD-NS, which considers only the dynamic change of the neighborhood sizes. The improved algorithm is applied to the practical problem of jointly retrieving atmospheric ducts with GNSS signals, and its performance further demonstrates its feasibility and practicability.
topic special sensor
GNSS
new algorithm
balance the diversity and convergence of the population
atmospheric ducts
url https://www.mdpi.com/1424-8220/20/8/2230
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