改进链式多种群遗传算法的防空火力任务分配

为化解敌方空袭的威胁，提高中等规模防空火力任务分配问题的求解效率，提出一种性能优越的链式多种群遗传算法(CMPGA)。首先建立改进的防空火力任务分配模型，综合考虑目标威胁程度、可拦截性判断等因素。目标威胁程度考虑目标的高度、速度、射程以及相对距离等威胁因素。可拦截性判断考虑时间约束、空间约束和性能约束，并将其融入杀伤概率的计算中，以简化模型的约束条件。其次提出CMPGA算法求解中等规模防空火力的最优任务分配方案。算法中综合运用了种群重复个体的数量限制策略、适应度相近个体的交叉变异策略、陷入局部极值时部分较优解的删除策略、链式环中种群当前最优解的传递策略。算法充分利用多种群并行搜索的优点，加快收敛速度，持续保持种群多样性，避免陷入局部极值。在标准测试函数的仿真以及防空火力任务分配问题的应用中，通过与几种典型优化算法的对比分析，结果表明CMPGA算法的性能优势较大，能以较高的概率快速地搜寻到最优解，从而验证了该算法的有效性和优越性。

Air defense firepower task assignment based on improved chainlike multi-population genetic algorithm

In view of the threat of enemy air attack and the efficiency of solving the task assignment problem of medium-scale air defense firepower, a chainlike multi-population genetic algorithm (CMPGA) with superior performance was proposed. First, an improved air defense firepower task allocation model was established, which comprehensively investigates the threat degree of target and the interceptability judgment. The target threat degree was studied in terms of the threat factors such as the height, speed, range, and relative distance of the target. The time constraints, space constraints, and performance constraints were considered in the interceptability judgment, which was integrated into the kill probability to simplify the constraints of the model. Then, the CMPGA algorithm was proposed to solve the optimal allocation scheme of medium-scale air defense firepower. The algorithm utilized the strategy of limiting the number of repetitive individuals in the population, the cross mutation strategy of individuals with similar fitness, the deletion strategy of partial optimal solution when falling into local extremum, and the transfer strategy of the current optimal solution in the chain link. Combining the advantages of multi-population parallel search, the algorithm could speed up convergence speed, maintain the diversity of population, and avoid falling into local extremum. In the simulation of standard test function and the application to air defense firepower task allocation problem, the proposed algorithm was compared with several typical optimization algorithms. Results show that the CMPGA algorithm had advantageous performance and could quickly find the optimal solution with high probability, which indicates the effectiveness and superiority of the algorithm.