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 muhi-objective muhi-sensor deployment based on muhi-objective local aberrance and adaptive quantum particle swarm optimization （LM-AQPSO） is proposed to study the deployment of muhi-sensors in complex terrain. The complex terrain is modeled by muhiattribute 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 muhi-objective LM-AQPSO algorithm is proposed for solving Pareto optimal solution. In considering the requirement of muhi-objective deployment, a muhisensor optimization deployment model based on Pareto optimal solution is established. Simulated resuhs 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 muhi-objective muhi-sensor deployment problem, and can provide more decision-making options.
作者
徐公国
段修生
单甘霖
童俊
XU Gong-guo;DUAN Xiu-sheng;SHAN Gan-lin;TONG Jun(Shijiazhuang Campus,Army Engineering University,Shijiazhuang 050003,Hebei,China;School of Mechanical Engineering,Shijiazhuang Tiedao University,Shijiazhuang 050043,Hebei,China)
出处
《兵工学报》
EI
CAS
CSCD
北大核心
2018年第11期2192-2201,共10页
Acta Armamentarii
基金
国防预先研究项目(012015012600A2203)
