基于启发式自适应离散差分进化算法的多UCAV协同干扰空战决策

 研究了多无人作战飞机(UCAV)协同干扰空战决策(MUCJAD)问题,在干扰效能评估指标量化方法的基础上为该问题建立了优化模型。为有效求解该模型,提出一种启发式自适应离散差分进化(H-SDDE)算法。在H-SDDE算法中,设计了包含4种候选解产生策略的候选策略池,引入了候选解产生策略及其参数的自适应学习过程。此外,结合实际问题为算法设计了基于威胁度的扩展型整数编码方案、基于威胁度的启发式个体调整操作、基于约束满足的个体修复操作。在12个测试实例上进行了仿真验证,结果表明,H-SDDE算法与其他同类算法相比在求解质量和求解速度上具有明显优势,能够更好地发挥多UCAV协同干扰整体效果。

Multiple UCAV Cooperative Jamming Air Combat Decision Making Based on Heuristic Self-adaptive Discrete Differential Evolution Algorithm

This paper presents a multiple unmanned combat air vehicle (UCAV) cooperative jamming air combat decision-making (MUCJAD) problem. An optimal model is constructed for the MUCJAD on the metric methods of the jamming effect evaluating indexes. In order to solve the model effectively, a heuristic self-adaptive discrete differential evolution (H-SDDE) algorithm is proposed in which, a strategy candidate pool which including four candidate solution generating strategies is designed and two self-adaptive learning processes are introduced for candidate solution generating strategy and its parameters. Furthermore, a threat degree based extensional integer coding scheme, a heuristic individual adjust operator and a constraints satisfaction based individual repair process are designed according to the real-word application. Simulation experiments are conducted on twelve test instances. The results indicate that the H-SDDE algorithm is better than other algorithms in terms of convergence speed and solution quality. The H-SDDE algorithm can enhance the effect of multiple UCAV cooperative jamming.