基于网络对抗火力分配的改进量子免疫克隆算法  

Improved Quantum Immune Clonic Algorithm in Weapon-Target Assignment under Conditions of Network Confrontation Environment

作　　者：冯超[1] 景小宁[1] 李秋妮 夏菲[2] 费凯[1] 

机构地区：[1]空军工程大学航空航天工程学院,西安710038 [2]国家电网辽阳供电公司,辽宁辽阳111000

出　　处：《空军工程大学学报（自然科学版）》2016年第4期29-34,共6页Journal of Air Force Engineering University(Natural Science Edition)

基　　金：国家自然科学基金(71501184);航空科学基金(20155196022)

摘　　要：考虑对于目标与目标组成网络的攻击总收益最大、自身消耗最小的原则,建立网络对抗条件下火力分配多目标优化模型,引入随机网络拓扑结构,分析火力分配方案对于随机网络的攻击效果,采用改进量子免疫克隆多目标算法对模型进行求解。通过实验仿真,分析攻击收益与不同弹药成本之间的变化情况,发现使用改进算法得到火力分配方案的攻击效率比标准算法平均高出23%;对算法的收敛性与Pareto解分布的均匀性进行研究,发现改进算法得到的Pareto解分布均匀性比标准算法提高了42%,验证了模型的有效性以及改进算法的优越性。In consideration of the principles that attack benefits of network combined targets with targets are maximal and its own consumption is minimal in total,a multi-obj ective optimization model is estab-lished under conditions of network confrontation environment in fire distribution.Under conditions of ran-dom network topology introduced,the effect of fire distribution corresponding to the random network is analyzed.This paper adopts quantum-inspired immune clonic multi-obj ective optimization algorithm to solve the model of fire distribution.Though experimental simulation,the change circumstances of the total attack benefits are analyzed by using different cost ammunition.The attack efficiency of the fire distribu-tion scheme increases by 23% by using the improved algorithm over the fire distribution scheme by using standard algorithm.The convergence of the algorithm and superiority of Pareto solution distribution are studied.The experiments demonstrate that the Pareto efficiency solution distribution increases 42% by u-sing the improved algorithm over using the standard algorithm.The superiority of the model and the effi-ciency of the algorithm are verified.

关 键 词：火力分配 量子克隆免疫多目标算法 复杂网络 网络中心战 PARETO最优解 

分 类 号：TP959.1[自动化与计算机技术]