Air combat maneuver decision-making based on improved symbiotic organisms search algorithm
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摘要:
针对现代空战机动决策问题，提出了一种基于改进共生生物搜索（SOS）算法的空战机动决策方法。首先，分析了传统基本机动动作库存在的不足，对其进行了改进和扩充，设计了11种常用的基本机动动作；然后，综合考虑角度、距离、速度、高度和战机性能优势，构造了战机机动决策优势函数；最后，针对传统共生生物搜索算法在收敛速度、收敛精度以及局部最优上存在的缺陷，将轮盘赌选择方法、动态变异率和梯度思想引入到传统算法当中，对算法有效性和算法性能进行了仿真分析。仿真结果表明，改进的共生生物搜索算法在收敛速度、收敛精度以及跳出局部最优上更具优势，能够满足空战机动决策需求。
Abstract:Aimed at the problem of modern air combat maneuver decision-making, an air combat maneuver decision-making method based on improved symbiotic organisms search (SOS) algorithm is proposed. Firstly, the shortcomings of the traditional basic maneuver inventory are analyzed, improved and expanded, and 11 kinds of common basic maneuver are designed. Secondly, considering the angle, distance, speed, altitude and the performance advantages of fighter planes, the decision-making advantage function of fighter planes is constructed. Finally, aimed at the shortcomings of the traditional SOS algorithm in convergence speed, convergence accuracy and local optimality, the roulette wheel selection method, dynamic variation rate and gradient idea are introduced into the traditional algorithm, and the effectiveness and performance of the algorithm are simulated and analyzed. The simulation results show that the improved SOS algorithm has more advantages in convergence speed, convergence accuracy and jump out of local optimum, and can meet the air combat maneuver decision-making requirements.
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图 3 敌我机几何态势划分示意图
Figure 3. Schematic of geometric situation division of enemy and our fighter
图 6 敌我双方空战对抗三维航迹展示
Figure 6. Three-dimensional track display of both sides in air combat
图 7 敌我双方航向角、飞行高度、飞行速度和空战优势变化曲线
Figure 7. Heading angle, height, speed and air combat superiority change curves of both sides
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