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兵工学报 ›› 2018, Vol. 39 ›› Issue (11): 2192-2201.doi: 10.3969/j.issn.1000-1093.2018.11.013

• 论文 • 上一篇    下一篇

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

徐公国1, 段修生1,2, 单甘霖1, 童俊1   

  1. (1.陆军工程大学石家庄校区, 河北 石家庄 050003; 2.石家庄铁道大学 机械工程学院, 河北 石家庄 050043)
  • 收稿日期:2018-02-04 修回日期:2018-02-04 上线日期:2018-12-25
  • 通讯作者: 段修生(1970—),男,教授,博士生导师 E-mail:sjzdxsh@163.com
  • 作者简介:徐公国(1991—),男,博士研究生。E-mail: xugguo@yeah.net
  • 基金资助:
    国防预先研究项目(012015012600A2203)

Optimization Deployment of Multi-sensors in Complex Terrain Based on Multi-objective LM-AQPSO Algorithm

XU Gong-guo1 , DUAN Xiu-sheng1,2, SHAN Gan-lin1, TONG Jun1   

  1. (1.Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, Hebei, China;2.School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, Hebei, China)
  • Received:2018-02-04 Revised:2018-02-04 Online:2018-12-25

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。

关键词: 传感器部署, 复杂地形, 多目标优化, 量子粒子群, Pareto最优解

Abstract: 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 multi-sensor 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. Key

Key words: sensordeployment, complexterrain, multi-objectiveoptimization, quantumparticleswarmoptimization, Paretooptimalsolution

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