Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/128216
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dc.contributor.authorNeshat, M.en
dc.contributor.authorAbbasnejad, E.en
dc.contributor.authorShi, Q.en
dc.contributor.authorAlexander, B.en
dc.contributor.authorWagner, M.en
dc.date.issued2019en
dc.identifier.citationProceedings of the 26th International Conference on Neural Information Processing (ICONIP 2019), as published in Lecture Notes in Computer Science (Neural Information Processing Proceedings, Part II), 2019 / Gedeon, T., Wong, K.W., Lee, M. (ed./s), vol.11954, pp.353-366en
dc.identifier.isbn9783030367107en
dc.identifier.issn0302-9743en
dc.identifier.issn1611-3349en
dc.identifier.urihttp://hdl.handle.net/2440/128216-
dc.description.abstractInstalled renewable energy capacity has expanded massively in recent years. Wave energy, with its high capacity factors, has great potential to complement established sources of solar and wind energy. This study explores the problem of optimising the layout of advanced, three-tether wave energy converters in a size-constrained farm in a numerically modelled ocean environment. Simulating and computing the complicated hydrodynamic interactions in wave farms can be computationally costly, which limits optimisation methods to using just a few thousand evaluations. For dealing with this expensive optimisation problem, an adaptive neuro-surrogate optimisation (ANSO) method is proposed that consists of a surrogate Recurrent Neural Network (RNN) model trained with a very limited number of observations. This model is coupled with a fast meta-heuristic optimiser for adjusting the model’s hyper-parameters. The trained model is applied using a greedy local search with a backtracking optimisation strategy. For evaluating the performance of the proposed approach, some of the more popular and successful Evolutionary Algorithms (EAs) are compared in four real wave scenarios (Sydney, Perth, Adelaide and Tasmania). Experimental results show that the adaptive neuro model is competitive with other optimisation methods in terms of total harnessed power output and faster in terms of total computational costs.en
dc.description.statementofresponsibilityMehdi Neshat, Ehsan Abbasnejad, Qinfeng Shi, Bradley Alexander, and Markus Wagneren
dc.language.isoenen
dc.publisherSpringer Natureen
dc.relation.ispartofseriesLecture Notes in Computer Science; 11954en
dc.rights© Springer Nature Switzerland AG 2019en
dc.source.urihttps://doi.org/10.1007/978-3-030-36711-4en
dc.subjectEvolutionary Algorithms; Local search; Surrogate-based optimisation; Sequential deep learning; Gray Wolf Optimiser; Wave Energy Converters; Renewable energyen
dc.titleAdaptive neuro-surrogate-based optimisation method for wave energy converters placement optimisationen
dc.typeConference paperen
dc.identifier.rmid1000011545en
dc.contributor.conference26th International Conference on Neural Information Processing (ICONIP) (12 Dec 2019 - 15 Dec 2019 : Sydney, Australia)en
dc.identifier.doi10.1007/978-3-030-36711-4_30en
dc.publisher.placeSwitzerlanden
dc.identifier.pubid480906-
pubs.library.collectionAustralian Institute for Machine Learning publicationsen
pubs.library.teamDS03en
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidNeshat, M. [0000-0002-9537-9513]en
dc.identifier.orcidShi, Q. [0000-0002-9126-2107]en
dc.identifier.orcidAlexander, B. [0000-0003-4118-2798]en
dc.identifier.orcidWagner, M. [0000-0002-3124-0061]en
Appears in Collections:Australian Institute for Machine Learning publications

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