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 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.

[1]  Yao Pei-yan.  Optimized action policy selection in task coalition evolution based on dynamic influence nets , 2014 .

[2]  Li Xiang-jun,et al.  On modeling and discrete particle swarm optimization for task assignment of cooperating UAVs , 2011, 2011 Chinese Control and Decision Conference (CCDC).

[3]  Alexander H. Levis,et al.  Creating executable models of influence nets with colored Petri nets , 1998, International Journal on Software Tools for Technology Transfer.

[4]  Sajjad Haider,et al.  Finding effective courses of action using particle swarm optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[5]  Zutong Wang,et al.  Uncertain UAV ISR mission planning problem with multiple correlated objectives , 2017, J. Intell. Fuzzy Syst..

[6]  Panayota Papantoni-Kazakos,et al.  Theory of Influence Networks , 2010, J. Intell. Robotic Syst..

[7]  Hong Liu,et al.  UAV route planning for aerial photography under interval uncertainties , 2016 .

[8]  Qiang Sun,et al.  A Study of Aviation Swarm Convoy and Transportation Mission , 2013, ICSI.

[9]  Dervis Karaboga,et al.  A comprehensive survey: artificial bee colony (ABC) algorithm and applications , 2012, Artificial Intelligence Review.

[10]  Kang Zhou,et al.  The Discrete Glowworm Swarm Optimization Algorithm with an Adaptive Neighborhood Search , 2016 .

[11]  W. Turechek.  Nonparametric tests in plant disease epidemiology: characterizing disease associations. , 2004, Phytopathology.