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dc.contributor.authorLiu, D.en
dc.contributor.authorPost, Elizabeth A.en
dc.contributor.authorKulasiri, Gamalathge D.en
dc.contributor.authorSherlock, R.en
dc.date.accessioned2013-05-02T00:31:26Z
dc.date.issued2003-07en
dc.identifier.citationLiu, D. S., Post, E., Kulasiri, D., Sherlock, R. A. (2003). Optimisation of a complex simulation model. In In Post, D. A. (ed) MODSIM 2003 International Congress on Modelling and Simulation: Integrative Modelling of Biophysical, Social, and Economic Systems for Resource Management Solutions. Modelling and Simulation Society of Australia and New Zealand, July 2003.en
dc.identifier.urihttps://hdl.handle.net/10182/5408
dc.description.abstractIn this paper we describe techniques utilised in the development of a scheme for identifying the regions in an 8-dimensional parameter space that gave optimal (or near-optimal) performance in a computational simulation of a real-world system. The system model, developed by Dexcel Ltd, attempts a detailed representation of pastoral dairying scenarios. It incorporates sub-models, themselves complex in many cases, of pasture growth, animal metabolism etc. Each evaluation of the objective function, a composite 'farm performance index', requires simulation of at least a one-year period of farm operation with a daily time-step and hence is computationally expensive. Since similar situations are likely to arise in other practical optimisation exercises, the results presented should have some quite general applicability.Two quite different methods of optimisation - Genetic Algorithm (GA) and Lipschitz Branch-and-Bound (LBB) algorithm are investigated and contrasted. Practical issues related to their efficient implementation in a Linux cluster parallel processing environment are discussed and their performance on the above problem is compared. The problem of visualisation of the objective function (response surface) in high-dimensional spaces is also considered in the context of the farm optimisation problem (where from a practical viewpoint knowledge of its behaviour in the region of optima is actually more important than the precise positions or values of the optima themselves). An adaption of the Parallel Coordinates visualization is described which helps visualise some important properties of the model’s output topography.en
dc.format.extent1817-1822en
dc.language.isoenen
dc.publisherModelling and Simulation Society of Australia and New Zealanden
dc.relationThe original publication is available from - Modelling and Simulation Society of Australia and New Zealanden
dc.rights© 2005 Modelling & Simulation Society of Australia & New Zealand Inc.en
dc.sourceMODSIM 2003 Proceedingsen
dc.subjectLipschitz Branch-and-Bounden
dc.subjectblack-box optimisationen
dc.subjectGenetic Algorithmen
dc.subjectparallel coordinatesen
dc.subjectblack-box optimizationen
dc.titleOptimisation of a complex simulation modelen
dc.typeConference Contribution - Published
lu.contributor.unitLincoln Universityen
lu.contributor.unitFaculty of Agriculture and Life Sciencesen
lu.contributor.unitDepartment of Wine, Food and Molecular Biosciencesen
lu.contributor.unitFaculty of Environment, Society and Designen
lu.contributor.unitDepartment of Environmental Managementen
pubs.finish-date2003-07-17en
pubs.organisational-group/LU
pubs.organisational-group/LU/Agriculture and Life Sciences
pubs.organisational-group/LU/Agriculture and Life Sciences/WFMB
pubs.organisational-group/LU/Faculty of Environment, Society and Design
pubs.organisational-group/LU/Faculty of Environment, Society and Design/DEM
pubs.organisational-group/LU/Research Management Office
pubs.organisational-group/LU/Research Management Office/QE18
pubs.publication-statusPublisheden
pubs.start-date2003-07-14en
pubs.volume4en
lu.identifier.orcid0000-0001-8744-1578
lu.subtypeConference Paperen


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