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dc.contributor.authorHameed, Ibrahim
dc.contributor.authorBye, Robin Trulssen
dc.contributor.authorOsen, Ottar
dc.date.accessioned2018-03-21T13:04:26Z
dc.date.available2018-03-21T13:04:26Z
dc.date.created2017-01-19T10:47:06Z
dc.date.issued2017
dc.identifier.citationAdvances in Intelligent Systems and Computing. 2017, 533 266-276.nb_NO
dc.identifier.issn2194-5357
dc.identifier.urihttp://hdl.handle.net/11250/2491506
dc.description.abstractOffshore crane design requires the configuration of a large set of design parameters in a manner that meets customers’ demands and operational requirements, which makes it a very tedious, time-consuming and expensive process if it is done manually. The need to reduce the time and cost involved in the design process encourages companies to adopt virtual prototyping in the design and manufacturing process. In this paper, we introduce a server-side crane prototyping tool able to calculate a number of key performance indicators of a specified crane design based on a set of about 120 design parameters. We also present an artificial intelligence client for product optimisation that adopts various optimization algorithms such as the genetic algorithm, particle swarm optimization, and simulated annealing for optimising various design parameters in a manner that achieves the crane’s desired design criteria (e.g., performance and cost specifications). The goal of this paper is to compare the performance of the aforementioned algorithms for offshore crane design in terms of convergence time, accuracy, and their suitability to the problem domain.nb_NO
dc.language.isoengnb_NO
dc.publisherSpringer Verlagnb_NO
dc.titleA comparison between optimization algorithms applied to offshore crane design using an online crane prototyping toolnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber266-276nb_NO
dc.source.volume533nb_NO
dc.source.journalAdvances in Intelligent Systems and Computingnb_NO
dc.identifier.doi10.1007/978-3-319-48308-5_26
dc.identifier.cristin1431830
dc.description.localcodeThis article will not be available due to copyright restrictions (c) 2017 by Springer Verlagnb_NO
cristin.unitcode194,63,55,0
cristin.unitnameInstitutt for IKT og realfag
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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