改进模拟退火遗传算法在协同空战中的应用

Application of improved simulated annealing genetic algorithm in cooperative air combat

针对不确定环境下的目标分配问题,本文建立了相应的评估及分配模型。构建空战态势评估指标体系,并用区间层次分析法(interval analytic hierarchy process,IAHP)和模糊优选法确定指标的主客观权重。然后建立相应的不确定环境下多目标优化攻击效能评估模型,将协同多目标攻击决策问题转化为导弹攻击配对优化问题。进一步提出一种基于区间灰数的模拟退火遗传算法(interval grey number-simulated annealing genetic algorithm,ISAGA)用于该决策问题的求解,设计了选择操作和相应的模拟退火机制,有效防止算法陷入局部最优。最后根据寻优所得的解确定合理的攻击方案。仿真结果表明:所提出的算法可以胜任不确定环境下目标分配问题的评估和寻优,并且在10步以内能够找到比较好的解,这在一定程度上是对传统的目标分配问题的完善。

The corresponding evaluation and assignment model is established to solve the problem of weapons target assignment in dynamic uncertain environments.Firstly,the index system of air combat situation assessment is established and the interval analytic hierarchy process and fuzzy optimization are used to obtain the subjective and objective weight of these indexes.Secondly,corresponding effectiveness evaluation mode of multi objective optimization is constructed with a dynamic environment,and the decision making problem for cooperative multiple target attack is converted to the optimization problem for missile attack matching.Furthermore,the interval grey number simulated annealing genetic algorithm is put forward to solve this problem.The proposed ISAGA designs a new selection operation and a special simulated annealing mechanism to enhance the global search ability.Finally,the reasonable attack scheme can be confirmed according to the optimal solution,and simulation results show that the proposed algorithm can be used to evaluate and optimize the weapons target assignment problem in uncertain environments and obtains a solution closer to the optimal solution in ten iterations,which is a consummation to some extent.