指挥控制与仿真
指揮控製與倣真
지휘공제여방진
COMMAND CONTROL & SIMULATION
2014年
1期
36-40
,共5页
优化部署%NSGA-Ⅱ算法%干扰环境%全局覆盖系数
優化部署%NSGA-Ⅱ算法%榦擾環境%全跼覆蓋繫數
우화부서%NSGA-Ⅱ산법%간우배경%전국복개계수
optimal deployment%NSGA-Ⅱ%jamming%global overlap index
在有源干扰条件下,雷达网部署直接影响着防区内指挥信息系统的预警监测能力。由于防区内由分散于不同位置,且重要度不同的责任区组成的,那么实现全方位全纵深的预警能力,将是雷达网部署的重要方面。根据覆盖系数和重叠系数为主要优化目标,基于NSGA-Ⅱ算法进行多目标优化。首先定义了覆盖系数和全局重叠系数两个指标,尤其是全局重叠系数打破了以往重叠系数的概念,从全局出发引导雷达网优化部署;同时,提出基于NSGA-Ⅱ的多目标优化部署算法,采用诱导跳跃、基因到位、诱导交叉等候选解生成方式,保持种群多样性,提高算法收敛性。实验表明,部署优化算法耗时较低,不同干扰源部署态势使网络节点部署产生较大差异,多样的候选解生成方法明显提高了算法的收敛速度。
在有源榦擾條件下,雷達網部署直接影響著防區內指揮信息繫統的預警鑑測能力。由于防區內由分散于不同位置,且重要度不同的責任區組成的,那麽實現全方位全縱深的預警能力,將是雷達網部署的重要方麵。根據覆蓋繫數和重疊繫數為主要優化目標,基于NSGA-Ⅱ算法進行多目標優化。首先定義瞭覆蓋繫數和全跼重疊繫數兩箇指標,尤其是全跼重疊繫數打破瞭以往重疊繫數的概唸,從全跼齣髮引導雷達網優化部署;同時,提齣基于NSGA-Ⅱ的多目標優化部署算法,採用誘導跳躍、基因到位、誘導交扠等候選解生成方式,保持種群多樣性,提高算法收斂性。實驗錶明,部署優化算法耗時較低,不同榦擾源部署態勢使網絡節點部署產生較大差異,多樣的候選解生成方法明顯提高瞭算法的收斂速度。
재유원간우조건하,뢰체망부서직접영향착방구내지휘신식계통적예경감측능력。유우방구내유분산우불동위치,차중요도불동적책임구조성적,나요실현전방위전종심적예경능력,장시뢰체망부서적중요방면。근거복개계수화중첩계수위주요우화목표,기우NSGA-Ⅱ산법진행다목표우화。수선정의료복개계수화전국중첩계수량개지표,우기시전국중첩계수타파료이왕중첩계수적개념,종전국출발인도뢰체망우화부서;동시,제출기우NSGA-Ⅱ적다목표우화부서산법,채용유도도약、기인도위、유도교차등후선해생성방식,보지충군다양성,제고산법수렴성。실험표명,부서우화산법모시교저,불동간우원부서태세사망락절점부서산생교대차이,다양적후선해생성방법명현제고료산법적수렴속도。
The optimal deployment of overlay network, which involves constructing overlay model, extracting multi-object function and designing optimal deployment algorithm, is one of the important parts in overlay network research. Generally speaking, the ranges of overlay network are often irregular closed graphs because of multifarious barriers. Consequently, we hope to put forward a fast algorithm based on marginal fitting to overcome a mass of wasting when calculating cover area. Sig-nificantly, the optimal deployment of overlay network in real world should take more aspects into consideration, including cover area, detection probability, and threaten degree of near link path which are different to formers. More attention, the near link path is aimed to analyze the relationship between dispersed remain areas in order to find out the probability of being a relative closed path which is uncovered by the overlay network. Finally, we put forward an optimal deployment algorithm based on NSGA-Ⅱ to deal with this problem, within different genetic operators such as attractive jumping, gene reversing and attractive overlapping to generate new candidates, and using global temperature to control generation and selection of candidates as Simulated Annealing. The marginal fitting algorithm was found highly accurate and fast for calculating the cover area, and the near link path discovered a different novel request in deployment, and the optimal deployment algorithm was proved to suit for overlay network deployment.