基于多目标局部变异-自适应量子粒子群优化算法的复杂地形多传感器优化部署
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摘要
对复杂地形下的多传感器部署问题进行研究,提出了基于多目标局部变异-自适应量子粒子群优化(LM-AQPSO)算法的多传感器多目标优化部署方法。该方法对复杂地形进行多属性网格建模,给出了传感器探测模型和优化目标。引进局部变异和参数自适应策略对量子粒子群优化算法进行改进,并提出了基于LM-AQPSO的多目标Pareto最优解集优化算法。考虑多目标部署需求,构建了基于Pareto最优解集的多传感器优化部署模型。仿真实验结果表明:相对于经典的改进非支配排序遗传算法,所提算法优化的Pareto最优解有着更好的收敛性和分布性,且寻优时间更短;所提模型能有效解决多目标多传感器部署问题,并能同时提供更多的决策方案。
A method of multi-objective multi-sensor deployment based on multi-objective local aberrance and adaptive quantum particle swarm optimization( LM-AQPSO) is proposed to study the deployment of multi-sensors in complex terrain. The complex terrain is modeled by multi-attribute grid technology,and the sensor detection model and optimization objectives are given. The QPSO algorithm is improved by utilizing local aberrance and adaptive strategy and a multi-objective LM-AQPSO algorithm is proposed for solving Pareto optimal solution. In considering the requirement of multi-objective deployment,a multisensor optimization deployment model based on Pareto optimal solution is established. Simulated results show that the Pareto optimal solutions obtained by LM-AQPSO algorithm have better convergence and distribution,and the optimization time is shorter compared with the classical non-dominated sorting genetic algorithm II. The proposed model can effectively deal with the multi-objective multi-sensor deployment problem,and can provide more decision-making options.
A method of multi-objective multi-sensor deployment based on multi-objective local aberrance and adaptive quantum particle swarm optimization( LM-AQPSO) is proposed to study the deployment of multi-sensors in complex terrain. The complex terrain is modeled by multi-attribute grid technology,and the sensor detection model and optimization objectives are given. The QPSO algorithm is improved by utilizing local aberrance and adaptive strategy and a multi-objective LM-AQPSO algorithm is proposed for solving Pareto optimal solution. In considering the requirement of multi-objective deployment,a multisensor optimization deployment model based on Pareto optimal solution is established. Simulated results show that the Pareto optimal solutions obtained by LM-AQPSO algorithm have better convergence and distribution,and the optimization time is shorter compared with the classical non-dominated sorting genetic algorithm II. The proposed model can effectively deal with the multi-objective multi-sensor deployment problem,and can provide more decision-making options.
引文
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[11]付英杰,汪沨,谭阳红.基于Pareto最优解的含分布式电源配电网无功优化[J].电力系统及其自动化学报,2017,29(1):18-23.FU Ying-jie,WANG Feng,TAN Yang-hong. Reactive power optimization of distribution network containing distributed generation based on Pareto optimal[J]. Proceedings of the CSU-EPSA,2017,29(1):18-23.(in Chinese)
[12]万旭光,毛樟根.偏好MOEA/D算法的研究与实现[J].现代电子技术,2018,41(1):133-138.WAN Xu-guang,MAO Zhang-gen. Research and implementation of preference MOEA/D algorithm[J]. Modern Electronics Technique,2018,41(1):133-138.(in Chinese)
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[2] Nayebi-Astaneh A,Pariz N,Naghibi-Sistani M B. Adaptive node scheduling under accuracy constraint for wireless sensor nodes with multiple bearings-only sensing units[J]. IEEE Transactions on Aerospace&Electronic Systems,2015,51(2):1547-1557.
[3] Guo J,Jafarkhani H. Sensor deployment with limited communication range in homogeneous and heterogeneous wireless sensor networks[J]. IEEE Transactions on Wireless Communications,2016,15(10):6771-6784.
[4] Alia O,Al-Ajouri A. Maximizing wireless sensor network coverage with minimum cost using harmony search algorithm[J]. IEEE Sensors Journal,2017,17:882-896.
[5]童俊,单甘霖.基于目标定位精度Cramér-Rao下限的多传感器空间布站研究[J].计算机测量与控制,2013,21(3):738-742,746.TONG Jun,SHAN Gan-lin. A study of multi-sensor space configuration optimization based on target position precision Cramér-Rao low bound[J]. Computer Measurement&Control,2013,21(3):738-742,746.(in Chinese)
[6] Sun Y,Li Y,Xiong W,et al. Pareto optimal solutions for network defense strategy selection simulator in multi-objective reinforcement learning[J]. Applied Sciences,2018,8(1):136-157.
[7]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):2085-2093.XIA Wei,LIU Xin-xue,FAN Yang-tao,et al. Weapon target assignment with an improved multi-objective particle swarm optimization algorithm[J]. Acta Armamentarii,2016,37(11):2085-2093.(in Chinese)
[8]梁兴,刘梅清,燕浩.基于Pareto最优解的梯级泵站双目标优化调度[J].武汉大学学报:工学版,2015,48(2):156-159,165.LIANG Xing,LIU Mei-qing,YAN Hao. The double objectives optimal scheduling of multistage pumping stations based on Pareto-optimal method[J]. Engineering Journal of Wuhan University,2015,48(2):156-159,165.(in Chinese)
[9]萧辉,杨国来,孙全兆.火炮多柔体动力学结构优化研究[J].兵工学报,2017,38(1):27-34.XIAO Hui,YANG Guo-lai,SUN Quan-zhao. Research on flexible multi-body dynamics structure optimization of artilleries[J]. Acta Armamentaeii,2017,38(1):27-34.(in Chinese)
[10]彭星光,高晓光,魏小丰.基于混合多目标进化算法的多无人机侦察路径规划[J].系统工程与电子技术,2010,32(2):326-331.PENG Xing-guang,GAO Xiao-guang,WEI Xiao-feng. Multiple UAVs routing in reconnaissance mission based on hybrid multiobjective evolutionary algorithm[J]. Systems Engineering and Electronics,2010,32(2):326-331.(in Chinese)
[11]付英杰,汪沨,谭阳红.基于Pareto最优解的含分布式电源配电网无功优化[J].电力系统及其自动化学报,2017,29(1):18-23.FU Ying-jie,WANG Feng,TAN Yang-hong. Reactive power optimization of distribution network containing distributed generation based on Pareto optimal[J]. Proceedings of the CSU-EPSA,2017,29(1):18-23.(in Chinese)
[12]万旭光,毛樟根.偏好MOEA/D算法的研究与实现[J].现代电子技术,2018,41(1):133-138.WAN Xu-guang,MAO Zhang-gen. Research and implementation of preference MOEA/D algorithm[J]. Modern Electronics Technique,2018,41(1):133-138.(in Chinese)
[13] Deb K,Pratap A,Agarwal S,et al. A fast and elitist multiobjective genetic algorithm:NSGA-II[J]. IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[14] Sun J,Xu W B,Feng B. A global search strategy of quantum-behaved particle swarm optimization[C]∥Proceedings of IEEE Conference on Cybernetics and Intelligent Systems. Singapore:IEEE,2004:111-116.