COA Optimized Selection Method of Aviation Swarm Based on DINs and DABC

Aiming at the selection problem for course of action (COA) of aviation swarm, this paper proposes an optimized selection method for COA of aviation swarm based on dynamic influence nets (DINs), and discrete artificial bee colony (DABC) algorithm. Firstly, based on the basic concept of the aviation s...

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Main Authors: Lujun Wan, Yun Zhong, Wen Li
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9044867/
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spelling doaj-bee2207145ea44619489b0ecc99547d32021-03-30T01:34:46ZengIEEEIEEE Access2169-35362020-01-018651166512610.1109/ACCESS.2020.29827849044867COA Optimized Selection Method of Aviation Swarm Based on DINs and DABCLujun Wan0https://orcid.org/0000-0003-2351-1148Yun Zhong1https://orcid.org/0000-0001-8164-9570Wen Li2Air Traffic Control and Navigation College, Air Force Engineering University, Xi’an, ChinaUnit 94062 of PLA, Korla, ChinaEquipment Management and UAV Engineering College, Air Force Engineering University, Xi’an, ChinaAiming at the selection problem for course of action (COA) of aviation swarm, this paper proposes an optimized selection method for COA of aviation swarm based on dynamic influence nets (DINs), and discrete artificial bee colony (DABC) algorithm. Firstly, based on the basic concept of the aviation swarm combat plan, static and dynamic modeling and analysis are performed, respectively. Then, the probability propagation mechanism of DINs, which mainly includes key parameter determination and probability propagation algorithm, is established. Subsequently, based on the analysis of the evaluation index, the model is solved by using DABC algorithm with real number coding. Finally, this paper takes the offshore island attack task as an example, and carries out multiple sets of simulation cases to compare DABC algorithm with discrete glowworm swarm optimization (DGSO) algorithm and discrete particle swarm optimization (DPSO) algorithm, through all these cases, the rationality of the model, and the effectiveness and superiority of the algorithm are verified.https://ieeexplore.ieee.org/document/9044867/Aviation swarmcourse of actiondynamic influence netsdiscrete artificial bee colony algorithmprobability propagation mechanism
collection DOAJ
language English
format Article
sources DOAJ
author Lujun Wan
Yun Zhong
Wen Li
spellingShingle Lujun Wan
Yun Zhong
Wen Li
COA Optimized Selection Method of Aviation Swarm Based on DINs and DABC
IEEE Access
Aviation swarm
course of action
dynamic influence nets
discrete artificial bee colony algorithm
probability propagation mechanism
author_facet Lujun Wan
Yun Zhong
Wen Li
author_sort Lujun Wan
title COA Optimized Selection Method of Aviation Swarm Based on DINs and DABC
title_short COA Optimized Selection Method of Aviation Swarm Based on DINs and DABC
title_full COA Optimized Selection Method of Aviation Swarm Based on DINs and DABC
title_fullStr COA Optimized Selection Method of Aviation Swarm Based on DINs and DABC
title_full_unstemmed COA Optimized Selection Method of Aviation Swarm Based on DINs and DABC
title_sort coa optimized selection method of aviation swarm based on dins and dabc
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description Aiming at the selection problem for course of action (COA) of aviation swarm, this paper proposes an optimized selection method for COA of aviation swarm based on dynamic influence nets (DINs), and discrete artificial bee colony (DABC) algorithm. Firstly, based on the basic concept of the aviation swarm combat plan, static and dynamic modeling and analysis are performed, respectively. Then, the probability propagation mechanism of DINs, which mainly includes key parameter determination and probability propagation algorithm, is established. Subsequently, based on the analysis of the evaluation index, the model is solved by using DABC algorithm with real number coding. Finally, this paper takes the offshore island attack task as an example, and carries out multiple sets of simulation cases to compare DABC algorithm with discrete glowworm swarm optimization (DGSO) algorithm and discrete particle swarm optimization (DPSO) algorithm, through all these cases, the rationality of the model, and the effectiveness and superiority of the algorithm are verified.
topic Aviation swarm
course of action
dynamic influence nets
discrete artificial bee colony algorithm
probability propagation mechanism
url https://ieeexplore.ieee.org/document/9044867/
work_keys_str_mv AT lujunwan coaoptimizedselectionmethodofaviationswarmbasedondinsanddabc
AT yunzhong coaoptimizedselectionmethodofaviationswarmbasedondinsanddabc
AT wenli coaoptimizedselectionmethodofaviationswarmbasedondinsanddabc
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