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Passive Location Resource Scheduling Based on an Improved Genetic Algorithm

With the development of science and technology, modern communication scenarios have put forward higher requirements for passive location technology. However, current location systems still use manual scheduling methods and cannot meet the current mission-intensive and widely-distributed scenarios, r...

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Detalles Bibliográficos
Autores principales: Jiang, Jianjun, Zhang, Jing, Zhang, Lijia, Ran, Xiaomin, Tang, Yanqun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6068865/
https://www.ncbi.nlm.nih.gov/pubmed/29966286
http://dx.doi.org/10.3390/s18072093
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author Jiang, Jianjun
Zhang, Jing
Zhang, Lijia
Ran, Xiaomin
Tang, Yanqun
author_facet Jiang, Jianjun
Zhang, Jing
Zhang, Lijia
Ran, Xiaomin
Tang, Yanqun
author_sort Jiang, Jianjun
collection PubMed
description With the development of science and technology, modern communication scenarios have put forward higher requirements for passive location technology. However, current location systems still use manual scheduling methods and cannot meet the current mission-intensive and widely-distributed scenarios, resulting in inefficient task completion. To address this issue, this paper proposes a method called multi-objective, multi-constraint and improved genetic algorithm-based scheduling (MMIGAS), contributing a centralized combinatorial optimization model with multiple objectives and multiple constraints and conceiving an improved genetic algorithm. First, we establish a basic mathematical framework based on the structure of a passive location system. Furthermore, to balance performance with respect to multiple measures and avoid low efficiency, we propose a multi-objective optimal function including location accuracy, completion rate and resource utilization. Moreover, to enhance its practicability, we formulate multiple constraints for frequency, resource capability and task cooperation. For model solving, we propose an improved genetic algorithm with better convergence speed and global optimization ability, by introducing constraint-proof initialization, a penalty function and a modified genetic operator. Simulations indicate the good astringency, steady time complexity and satisfactory location accuracy of MMIGAS. Moreover, compared with manual scheduling, MMIGAS can improve the efficiency while maintaining high location precision.
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spelling pubmed-60688652018-08-07 Passive Location Resource Scheduling Based on an Improved Genetic Algorithm Jiang, Jianjun Zhang, Jing Zhang, Lijia Ran, Xiaomin Tang, Yanqun Sensors (Basel) Article With the development of science and technology, modern communication scenarios have put forward higher requirements for passive location technology. However, current location systems still use manual scheduling methods and cannot meet the current mission-intensive and widely-distributed scenarios, resulting in inefficient task completion. To address this issue, this paper proposes a method called multi-objective, multi-constraint and improved genetic algorithm-based scheduling (MMIGAS), contributing a centralized combinatorial optimization model with multiple objectives and multiple constraints and conceiving an improved genetic algorithm. First, we establish a basic mathematical framework based on the structure of a passive location system. Furthermore, to balance performance with respect to multiple measures and avoid low efficiency, we propose a multi-objective optimal function including location accuracy, completion rate and resource utilization. Moreover, to enhance its practicability, we formulate multiple constraints for frequency, resource capability and task cooperation. For model solving, we propose an improved genetic algorithm with better convergence speed and global optimization ability, by introducing constraint-proof initialization, a penalty function and a modified genetic operator. Simulations indicate the good astringency, steady time complexity and satisfactory location accuracy of MMIGAS. Moreover, compared with manual scheduling, MMIGAS can improve the efficiency while maintaining high location precision. MDPI 2018-06-29 /pmc/articles/PMC6068865/ /pubmed/29966286 http://dx.doi.org/10.3390/s18072093 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jiang, Jianjun
Zhang, Jing
Zhang, Lijia
Ran, Xiaomin
Tang, Yanqun
Passive Location Resource Scheduling Based on an Improved Genetic Algorithm
title Passive Location Resource Scheduling Based on an Improved Genetic Algorithm
title_full Passive Location Resource Scheduling Based on an Improved Genetic Algorithm
title_fullStr Passive Location Resource Scheduling Based on an Improved Genetic Algorithm
title_full_unstemmed Passive Location Resource Scheduling Based on an Improved Genetic Algorithm
title_short Passive Location Resource Scheduling Based on an Improved Genetic Algorithm
title_sort passive location resource scheduling based on an improved genetic algorithm
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6068865/
https://www.ncbi.nlm.nih.gov/pubmed/29966286
http://dx.doi.org/10.3390/s18072093
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