Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm opt...Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.展开更多
An intelligent flow control on the flow separation over an airfoil under weak turbulent conditions is investigated and solved by deep reinforcement learning(DRL)method.Both single and synthetic jet control at the airf...An intelligent flow control on the flow separation over an airfoil under weak turbulent conditions is investigated and solved by deep reinforcement learning(DRL)method.Both single and synthetic jet control at the airfoil angles of attack of 10°,13°,15°are compared by training a neural network for closed-loop active flow control strategy based on the soft actor-critic(SAC)algorithm.The training results demonstrate the effectiveness of the deep reinforcement learning-based active flow control method in suppressing the flow separation at high angles of attack,validating its potential in complex flow environments.To improve the stability of the shedding vortex alley over airfoil,a novel reward function considering the vorticity statistics in terms of both vortex and asymmetric shear intensity is first proposed in this work.This vorticity driven reward is demonstrated to perform better in suppressing the rotation and shear intensity and the aerodynamic optimization than the traditional one.Moreover,it can accelerate the convergence speed during the exploration phase.Moreover,it can accelerate the convergence speed during the exploration phase.This study provides valuable insights for future applications of DRL in active flow control under more complex flow conditions.展开更多
In order to achieve the optimal attack outcome in the air combat under the beyond visual range(BVR)condition,the decision-making(DM)problem which is to set a proper assignment for the friendly fighters on the hostile ...In order to achieve the optimal attack outcome in the air combat under the beyond visual range(BVR)condition,the decision-making(DM)problem which is to set a proper assignment for the friendly fighters on the hostile fighters is the most crucial task for cooperative multiple target attack(CMTA).In this paper,a heuristic quantum genetic algorithm(HQGA)is proposed to solve the DM problem.The originality of our work can be supported in the following aspects:(1)the HQGA assigns all hostile fighters to every missile rather than fighters so that the HQGA can encode chromosomes with quantum bits(Q-bits);(2)the relative successful sequence probability(RSSP)is defined,based on which the priority attack vector is constructed;(3)the HQGA can heuristically modify quantum chromosomes according to modification technique proposed in this paper;(4)last but not the least,in some special conditions,the HQGA gets rid of the constraint described by other algorithms that to obtain a better result.In the end of this paper,two examples are illustrated to show that the HQGA has its own advantage over other algorithms when dealing with the DM problem in the context of CMTA.展开更多
文摘Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.
基金Project supported by the National Natural Science Foundation of China(Grant No.52476033),the 2021 Shanghai Chenguang Program(Grant No.21CGA54).
文摘An intelligent flow control on the flow separation over an airfoil under weak turbulent conditions is investigated and solved by deep reinforcement learning(DRL)method.Both single and synthetic jet control at the airfoil angles of attack of 10°,13°,15°are compared by training a neural network for closed-loop active flow control strategy based on the soft actor-critic(SAC)algorithm.The training results demonstrate the effectiveness of the deep reinforcement learning-based active flow control method in suppressing the flow separation at high angles of attack,validating its potential in complex flow environments.To improve the stability of the shedding vortex alley over airfoil,a novel reward function considering the vorticity statistics in terms of both vortex and asymmetric shear intensity is first proposed in this work.This vorticity driven reward is demonstrated to perform better in suppressing the rotation and shear intensity and the aerodynamic optimization than the traditional one.Moreover,it can accelerate the convergence speed during the exploration phase.Moreover,it can accelerate the convergence speed during the exploration phase.This study provides valuable insights for future applications of DRL in active flow control under more complex flow conditions.
基金supported by National Nature Science Foundation of China,and the supporting project is“Study on parallel intelligent optimization simulation with combination of qualitative and quantitative method”(61004089)supported by the Graduate Student Innovation Practice Foundation of Beihang University in China(YCSJ-01-201205),which is“Research of an efficient and intelligent optimization method and application in aircraft shape design”.
文摘In order to achieve the optimal attack outcome in the air combat under the beyond visual range(BVR)condition,the decision-making(DM)problem which is to set a proper assignment for the friendly fighters on the hostile fighters is the most crucial task for cooperative multiple target attack(CMTA).In this paper,a heuristic quantum genetic algorithm(HQGA)is proposed to solve the DM problem.The originality of our work can be supported in the following aspects:(1)the HQGA assigns all hostile fighters to every missile rather than fighters so that the HQGA can encode chromosomes with quantum bits(Q-bits);(2)the relative successful sequence probability(RSSP)is defined,based on which the priority attack vector is constructed;(3)the HQGA can heuristically modify quantum chromosomes according to modification technique proposed in this paper;(4)last but not the least,in some special conditions,the HQGA gets rid of the constraint described by other algorithms that to obtain a better result.In the end of this paper,two examples are illustrated to show that the HQGA has its own advantage over other algorithms when dealing with the DM problem in the context of CMTA.
