基于改进量子免疫克隆多目标优化算法的火力分配问题

Weapon-target assignment based on improved quantum-inspired immune clonal multi-objective optimization algorithm

针对传统火力分配中存在武器资源浪费的情况,以对敌目标与网络攻击收益最大、己方武器消耗最小为目标,建立一种考虑毁伤概率约束条件的多目标火力分配模型。对标准量子免疫克隆多目标优化算法进行优化,引入了混沌机制,修复不可行解,并对搜索策略和多样性保持策略进行改进,设计了一种改进的量子免疫克隆多目标优化算法。通过实验仿真,验证了模型的正确性与算法的优越性。相比于传统量子免疫克隆算法,改进算法的性能平均提高了23%。

Aiming at the problem of waste of weapon resources in traditional weapon target assign- ment （WTA）, and to realize the maximum benefit of target and network attacks and the minimum con- sumption of weapons, we establish a multi-objective WTA model, which takes the damage probability constraint into consideration. We improve the standard quantum immune clonal multi-objective optimiza- tion algorithm （QICMOA）, introduce the chaos mechanism, and repair the infeasible solutions. We also make improvement for the search strategy and diversity maintaining strategy and design an improved QICMOA. Simulation experiments verify the correctness of the model and the superiority of the algo- rithm. Compared with the traditional QICMOA, the performance of the new algorithm is improved by 23% on average.