Cooperative scheduling of imaging observation tasks for high-altitude airships based on propagation algorithm.
The cooperative scheduling problem on high-altitude airships for imaging observation tasks is discussed. A constraint programming model is established by analyzing the main constraints, which takes the maximum task benefit and the minimum cruising distance as two optimization objectives. The coopera...
| Publicado en: | Scientific World Journal pp. 548250 - 548251 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104308129&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104308129 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2012 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104308129 NLM23365522 2011924665 10.1100/2012/548250 NLM23365522 PMC3533461 104308129 ppf: 548250 ppct: 1 formats: tig: atl: Cooperative scheduling of imaging observation tasks for high-altitude airships based on propagation algorithm. aug: au: Chuan, He Dishan, Qiu Jin, Liu affil: Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China. sug: subj: Aircraft Algorithms Altitude Cooperative Behavior Aviation Methods Pilot Studies Human Models, Statistical Models, Theoretical Reproducibility of Results Time Factors ab: The cooperative scheduling problem on high-altitude airships for imaging observation tasks is discussed. A constraint programming model is established by analyzing the main constraints, which takes the maximum task benefit and the minimum cruising distance as two optimization objectives. The cooperative scheduling problem of high-altitude airships is converted into a main problem and a subproblem by adopting hierarchy architecture. The solution to the main problem can construct the preliminary matching between tasks and observation resource in order to reduce the search space of the original problem. Furthermore, the solution to the sub-problem can detect the key nodes that each airship needs to fly through in sequence, so as to get the cruising path. Firstly, the task set is divided by using k-core neighborhood growth cluster algorithm (K-NGCA). Then, a novel swarm intelligence algorithm named propagation algorithm (PA) is combined with the key node search algorithm (KNSA) to optimize the cruising path of each airship and determine the execution time interval of each task. Meanwhile, this paper also provides the realization approach of the above algorithm and especially makes a detailed introduction on the encoding rules, search models, and propagation mechanism of the PA. Finally, the application results and comparison analysis show the proposed models and algorithms are effective and feasible. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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