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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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 |
_version_ |
1724186827876728832 |