An improved genetic algorithm of multiple cooperative air combat fire distribution method

Author(s):  
Liang Ma ◽  
Shu Qi Wang
Author(s):  
Jianfeng Xie ◽  
Qiming Yang ◽  
Shuling Dai ◽  
Wanyang Wang ◽  
Jiandong Zhang

With the continuous development of UAV technology, the trend of using UAV in the military battlefield is increasingly obvious, but the autonomous air combat capability of UAV needs to be further improved. The air combat maneuvering decision is the key link to realize the UAV autonomous air combat, and the genetic algorithm has good robustness and global searching ability which is suitable for solving large-scale optimization problems. This paper uses an improved genetic algorithm to model UAV air combat maneuvering decisions. Based on engineering application requirements, a typical simulation test scenario is established. The simulation results show that the air combat maneuvering decision model based on reinforcement genetic algorithm in this paper can obtain the correct maneuvering decision sequence and gain a position advantage in combat.


2021 ◽  
pp. 3699-3709
Author(s):  
Yangming Kang ◽  
Zhiqiang Pu ◽  
Zhen Liu ◽  
Gui Li ◽  
Ruiyan Niu ◽  
...  

2013 ◽  
Vol 433-435 ◽  
pp. 720-724
Author(s):  
Hong Xia Liu ◽  
Xin Chen

The central issue of finishing train is that we should distribute the thickness of each exit with reason and determine the rolling force and relative convexity. The optimization methods currently used are empirical distribution method and the load curve method, but they both have drawbacks. To solve those problems we established a mathematical model of the finishing train and introduced an improved Genetic Algorithm. In this algorithm we used real number encoding, selection operator of a roulette and elitist selection and then improved crossover and mutation operators. The results show that the model and algorithm is feasible and could ensure the optimal effect and convergence speed. The products meet the production requirements.


Author(s):  
Ge Weiqing ◽  
Cui Yanru

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.


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