COA optimized selection method of aviation swarm based on DINs and DABC

• Published in: IEEE access Vol. 8; p. 1
• Main Authors: Wan, Lujun, Zhong, Yun, Li, Wen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analytical models; Aviation; Aviation swarm; Benchmark testing; Cognition; course of action; discrete artificial bee colony algorithm; dynamic influence nets; Dynamic models; Heuristic algorithms; Mathematical model; Particle swarm optimization; probability propagation mechanism; Programmable logic arrays; Propagation; Task analysis
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2982784

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) algorighm 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.