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dc.contributor.authorNamazi, Majid
dc.contributor.authorSanderson, Conrad
dc.contributor.authorNewton, MA Hakim
dc.contributor.authorSattar, Abdul
dc.date.accessioned2020-07-30T06:31:58Z
dc.date.available2020-07-30T06:31:58Z
dc.date.issued2020
dc.identifier.urihttp://hdl.handle.net/10072/395953
dc.description.abstractThe travelling thief problem (TTP) is a multi-component optimisation problem involving two interdependent NP-hard components: the travelling salesman problem (TSP) and the knapsack problem (KP). Recent state-of-the-art TTP solvers modify the underlying TSP and KP solutions in an iterative and interleaved fashion. The TSP solution (cyclic tour) is typically changed in a deterministic way, while changes to the KP solution typically involve a random search, effectively resulting in a quasi-meandering exploration of the TTP solution space. Once a plateau is reached, the iterative search of the TTP solution space is restarted by using a new initial TSP tour. We propose to make the search more efficient through an adaptive surrogate model (based on a customised form of Support Vector Regression) that learns the characteristics of initial TSP tours that lead to good TTP solutions. The model is used to filter out non-promising initial TSP tours, in effect reducing the amount of time spent to find a good TTP solution. Experiments on a broad range of benchmark TTP instances indicate that the proposed approach filters out a considerable number of non-promising initial tours, at the cost of omitting only a small number of the best TTP solutions.
dc.description.peerreviewedYes
dc.publisherAssociation for the Advancement of Artificial Intelligence (AAAI)
dc.publisher.urihttps://aaai.org/Library/SOCS/socs20contents.php
dc.relation.ispartofconferencename13th Annual Symposium on Combinatorial Search (SoCS 2020)
dc.relation.ispartofdatefrom2020-05-26
dc.relation.ispartofdateto2020-05-28
dc.relation.ispartoflocationVienna, Austria
dc.relation.ispartofseriesProceedings of the 13th International Symposium on Combinatorial Search (SoCS 2020)
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchOperations Research
dc.subject.fieldofresearchSignal Processing
dc.subject.fieldofresearchcode080109
dc.subject.fieldofresearchcode010206
dc.subject.fieldofresearchcode090609
dc.titleSurrogate Assisted Optimisation for Travelling Thief Problems
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationNamazi, M; Sanderson, C; Newton, MAH; Sattar, A, Surrogate Assisted Optimisation for Travelling Thief Problems, Proceedings of the 13th International Symposium on Combinatorial Search (SoCS 2020), 2020
dc.date.updated2020-07-29T03:30:26Z
dc.description.versionPost-print
gro.rights.copyright© 2020 AAAI Press. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
gro.hasfulltextFull Text
gro.griffith.authorSanderson, Conrad
gro.griffith.authorNamazi, Majid
gro.griffith.authorNewton, MAHakim A.
gro.griffith.authorSattar, Abdul


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