基于离散映射的量子粒子群优化算法求解WTA问题
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摘要
针对武器目标分配(WTA)问题及其特点,提出一种基于离散映射的量子粒子群优化算法。通过武器系统对目标攻击过程中得到的毁伤收益建立了目标分配模型,提出一种基于离散映射的编码调整方式,将连续型粒子位置矢量投影至离散空间上,避免产生不满足模型约束条件的非法解,从而提高粒子利用率。通过仿真对比验证,论文算法具有较高的收敛速度与稳定性,表明该方法能有效求解WTA问题。
In view of the weapon target assignment(WTA) problem and its characteristics,this paper puts forward a quantum particle swarm optimization algorithm with discrete mapping. First of all,a target allocation model is established for the damage benefit obtained by the weapon system in the target attack process. A method of coding adjustment based on discrete mapping is proposed to project the continuous particle position vector onto the discrete space to avoid the noncompliance of the model constraint solution for improving particle utilization. Through comparison of simulation validation,the improved algorithm has superior speed of convergence and stability. The experimental results show that the proposed method can effectively solve the WTA problem.
In view of the weapon target assignment(WTA) problem and its characteristics,this paper puts forward a quantum particle swarm optimization algorithm with discrete mapping. First of all,a target allocation model is established for the damage benefit obtained by the weapon system in the target attack process. A method of coding adjustment based on discrete mapping is proposed to project the continuous particle position vector onto the discrete space to avoid the noncompliance of the model constraint solution for improving particle utilization. Through comparison of simulation validation,the improved algorithm has superior speed of convergence and stability. The experimental results show that the proposed method can effectively solve the WTA problem.
引文
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[3]邓道靖,马云红,龚洁,等.基于并行GAPSO算法的多无人机协同任务规划[J].电光与控制,2016,23(11):18-22.Deng Daojing,Ma Yunhong,Gong Jie,et al.Cooperative Mission Planning of Multiple UAVs Based on Parallel GAPSO Algorithm[J].Electronics Optics&Control,2016,23(11):18-22.(in Chinese)
[4]夏维,刘新学,范阳涛,等.基于改进型多目标粒子群优化算法的武器-目标分配[J].兵工学报,2016,37(11):2085-2092.Xia Wei,Liu Xinxue,Fan Yangtao,et al.Weapon-Target Assignment with an Improved Multi-Objective Particle Swarm Optimization Algorithm[J].Acta Armamentarii,2016,37(11):2085-2092.(in Chinese)
[5]王强,张安,宋志蛟.UAV协同任务分配的改进DPSO算法仿真研究[J].系统仿真学报,2014,26(5):1149-1155.Wang Qiang,Zhang An,Song Zhijiao.Simulation Study on Improved Discrete Particle Swarm Optimization Algorithm for Multiple UAV Cooperation Task Assignment[J].Journal of System Simulation,2014,26(5):1149-1155.(in Chinese)
[6]王一川,单甘霖,童俊.基于协同Memetic PSO算法的传感器-目标分配问题求解[J].系统工程与电子技术,2013,35(5):1000-1007.Wang Yichuan,Shan Ganlin,Tong Jun.Solving SensorTarget Assignment Problem Based on Cooperative Memetic PSO Algorithm[J].Systems Engineering and Electronics,2013,35(5):1000-1007.(in Chinese)
[7]范成礼,邢清华,郑明发,等.基于IDPSO的武器目标分配优化算法[J].系统工程与电子技术,2015,37(2):336-342.Fan Chengli,Xing Qinghua,Zheng Mingfa,et al.Weapon-Target Allocation Optimization Algorithm Based on IDPSO[J].Systems Engineering and Electronics,2015,37(2):336-342.(in Chinese)
[8]段修生,徐公国,单甘霖.基于协同Memetic自适应QPSO算法的传感器-目标分配问题求解[J].系统工程与电子技术,2016,38(12):2769-2776.Duan Xiusheng,Xu Gongguo,Shan Ganlin.Solution to Sensor-Target Assignment Problem Based on Cooperative Memetic Adaptive QPSO Algorithm[J].Systems Engineering and Electronics,2016,38(12):2769-2776.(in Chinese)
[9]王晓光,章卫国,陈伟.无人机编队超视距空战决策及作战仿真[J].控制与决策,2015,30(2):328-334.Wang Xiaoguang,Zhang Weiguo,Chen Wei.BVR Air Combat Decision Making and Simulation for Formation[J].Control and Decision,2015,30(2):328-334.(in Chinese)
[10]孙俊.量子行为粒子群优化算法研究[D].无锡:江南大学,2009.Sun Jun.Study on Quantum-Behaved Particle Swarm Optimization Algorithm[D].Wuxi:Jiangnan University,2009.(in Chinese)