新混合智能计算法在UCAV航路规划中的应用

Application of a New Hybrid Intelligent Computation Method in Path Planning of UCAV

无人作战飞机(UCAV)出航执行对地攻击(或侦察)任务,若事先针对敌方防御区内的威胁部署和目标的分布情况,就飞行航路进行整体规划设计,则可以综合减小被敌方发现和反击的可能性,最大限度地降低耗油量,从而显著提高其执行任务的成功率。在对进化算法研究的基础上,将用于解决旅行商问题(TSP)的进化算法加以改进,引入优秀个体保护法和模拟退火的策略思想,借以克服进化算法固有的易陷于局部最优的早熟现象,然后运用于UCAV的航路规划。实验结果表明,改进的混合智能计算方法简易而有效,寻优效果明显优于常规进化算法,规划出的航路能够满足UCAV飞行任务规划的综合需要。

While Unmanned Combating Air Vehicle （UCAV） carries out attack or reconnaissance tasks against ground targets, the off-line global path planning, based on threat deployment and target distribution in the defense areas of enemies, can minimize the possibility of being detected by enemies and also reduce the fuel consumption entirely. As a result, the success probability of mission can be expected to be greatly increased. On the basis of analysis on Evolutionary Algorithm （EA）, a new EA for solving this problem is presented in this paper, in which both the strategies of elitism and simulated annealing are introduced in order to keep balance between speed-up of convergence and avoiding of premature of EA. The modified method is applied to UCAV path planning. The experimental results demonstrate that this hybrid computational intelligence method, which is simple and effective, and superior to that of traditional EAs. The planned path can meet the demands of UCAV flight mission planning.