Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/133576
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dc.contributor.authorNeshat, M.-
dc.contributor.authorAlexander, B.-
dc.contributor.authorWagner, M.-
dc.contributor.authorXia, Y.-
dc.contributor.editorAguirre, H.E.-
dc.contributor.editorTakadama, K.-
dc.date.issued2018-
dc.identifier.citationProceedings of the 2018 Genetic and Evolutionary Computation Conference as published in GECCO 2018, 2018 / Aguirre, H.E., Takadama, K. (ed./s), pp.1318-1325-
dc.identifier.isbn9781450356183-
dc.identifier.urihttps://hdl.handle.net/2440/133576-
dc.description.abstractIn order to address environmental concerns and meet growing energy demand the development of green energy technology has expanded tremendously. One of the most promising types of renewable energy is ocean wave energy. While there has been strong research in the development of this technology to date there remain a number of technical hurdles to overcome. This research explores a type of wave energy converter (WEC) called a buoy. This work models a power station as an array of fully submerged three-tether buoys. The target problem of this work is to place buoys in a size constrained environment to maximise power output. This article improves prior work by using a more detailed model and exploring the search space using a wide variety of search heuristics. We show that a hybrid method of stochastic local search combined with Nelder-Mead Simplex direct search performs better than previous search techniques.-
dc.description.statementofresponsibilityMehdi Neshat, Bradley Alexander, Markus Wagner, Yuanzhong Xia-
dc.language.isoen-
dc.publisherACM-
dc.rights© 2018 Association for Computing Machinery.-
dc.source.urihttps://doi.org/10.1145/3205455-
dc.titleA detailed comparison of meta-heuristic methods for optimising wave energy converter placements-
dc.typeConference paper-
dc.contributor.conferenceGECCO '18: Genetic and Evolutionary Computation Conference (15 Jul 2018 - 19 Jul 2018 : Kyoto)-
dc.identifier.doi10.1145/3205455.3205492-
dc.publisher.placeonline-
dc.relation.granthttp://purl.org/au-research/grants/arc/DE160100850-
dc.relation.granthttp://purl.org/au-research/grants/arc/DE160100850-
pubs.publication-statusPublished-
dc.identifier.orcidNeshat, M. [0000-0002-9537-9513]-
dc.identifier.orcidAlexander, B. [0000-0003-4118-2798]-
dc.identifier.orcidWagner, M. [0000-0002-3124-0061]-
Appears in Collections:Computer Science publications

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