Show simple item record

dc.contributor.authorRawlins, T
dc.contributor.authorLewis, A
dc.contributor.authorHettenhausen, J
dc.contributor.authorKipouros, T
dc.contributor.editorAmir Hussain
dc.date.accessioned2017-12-01T00:30:55Z
dc.date.available2017-12-01T00:30:55Z
dc.date.issued2015
dc.identifier.isbn9781479919604
dc.identifier.doi10.1109/IJCNN.2015.7280520
dc.identifier.urihttp://hdl.handle.net/10072/125373
dc.description.abstractArtificial Neural Networks (ANNs) have often been used to model objective functions for Multi-Objective Particle Swarm Optimisation (MOPSO); alternatively MOPSO has been used to aid in training ANNs. In previous work we instead used an ANN to guide optimisation by deciding if a trial solution was worthy of full evaluation. In this work we introduce Active Learning to the ANN-guided MOPSO. This is done by using a dynamic subset of particles from the MOPSO swarm to classify locations that are likely to be on the boundary between feasible and infeasible space. As a case study we sought to optimise the shape of an airfoil to minimise drag and maximise lift.We investigated the effect of allowing up to 20 particles from the swarm to be used for Active Learning. Our analysis showed the addition of Active Learning resulted in an increase in performance where an initial archive for training was available. However if an initial archive was not available then Active Learning performed at best equal to non-Active Learning and often worse, in some cases showing poorer performance than an unguided MOPSO.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencenameIJCNN 2015
dc.relation.ispartofconferencetitleProceedings of the International Joint Conference on Neural Networks
dc.relation.ispartofdatefrom2015-07-12
dc.relation.ispartofdateto2015-07-17
dc.relation.ispartoflocationKillarney, Ireland
dc.relation.ispartofvolume2015-September
dc.subject.fieldofresearchArtificial intelligence not elsewhere classified
dc.subject.fieldofresearchcode460299
dc.titleEnhancing ANN-guided MOPSO through Active Learning
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorLewis, Andrew J.
gro.griffith.authorRawlins, Tim
gro.griffith.authorHettenhausen, Jan
gro.griffith.authorKipouros, Timos


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record