多UCAV任务分配的混合遗传算法与约束处理

Hybrid Genetic Algorithm and Constraint Handling for Multiple UCAV Mission Assigning

针对多UCAV协同作战任务分配问题,建立了多目标整数规划模型,提出了基于整数编码的混合遗传算法.将约束分为全局约束和局部约束,根据局部约束将决策变量分为自由变量和非自由变量,仅对非自由变量进行编码,减少了染色体变化要素.设计了交叉算子和变异算子,以提高个体的约束满足率.以UCAV的SEAD任务为想定进行仿真,实验结果表明,该混合遗传算法可有效解决大规模整数规划问题,在求解效率和约束满足率上比标准遗传算法有显著提高.

To resolve the multiple cooperative UCAV mission assigning problem, a multi-object integer programming model is presented, and a hybrid genetic algorithm is proposed. The constraints are sorted into global ones and partial ones. According to the partial constraints, the decision variables are divided into free and non-free ones. Only non-free variables are coded to further lessen the chromosome length and to decrease the alterable elements. Then with the partial constraints, the crossover and mutation operators are designed which increas the variables satisfying constraint probability. The simulation results under the SEAD scenario show that the hybrid genetic algorithm resolves the UCAV mission assigning effectively, and has better efficiency and higher chromosome satisfying constraints probabilities than the standard genetic algorithm.