基于混合遗传算法的多UCAV协同任务分配方法

针对多UCAV协同作战的控制决策问题,提出了多UCAV任务分配的多目标整数规划模型.将问题的启发性知识融合进遗传算法,提出了求解该问题的基于整数编码的混合遗传算法.将变量根据约束条件特点分为自由变量和非自由变量,仅对非自由变量编码,减小了染色体长度和变化要素,从而提高了算法的效率.设计了适于整数编码的交叉和变异算子,并巧妙地利用约束条件限制变异范围,以提高个体满足约束条件的概率.以UCAV的SEAD任务为想定,进行了仿真实验.实验结果表明混合遗传算法可以有效地解决大规模整数规划问题,在求解效率和提高约束条件满足率上优于标准遗传算法.

Hybrid genetic algorithm based method for multiple cooperative UCAVs mission assigning

For control and management of multiple cooperative uninhabited combat air vehicle(UCAVs),a multi-object integer programming mathematic model is formulized to solve the UCAVs mission assigning problem.Combining with the heuristic knowledge,an integer coding based hybrid genetic algorithm for the mission assigning problem is proposed.According to the constraints,the variables are divided into non-free variables and free ones.Only the non-free variables are coded as chromosomes to lessen the chromosome length and to decrease the alterable elements.Then,the crossover opera and mutation opera fit for the integer coding are designed,and the mutating range are limited by using the constraints to increase the probability of individuals satisfying constraints.Under the scenario of UCAVs suppression of enemy air defense(SEAD),simulation experiments are made.The simulation results show that the hybrid genetic algorithm can resolve the mission assigning for multiple cooperative UCAVs effectively.Compared with the standard genetic algorithm,it has better efficiency and higher chromosome satisfying constraints probabilities.