Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/79233
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dc.contributor.authorRobic, Florian-
dc.contributor.authorMicallef, Daniel-
dc.contributor.authorBorg, Simon Paul-
dc.contributor.authorEllul, Brian-
dc.date.accessioned2021-08-03T10:09:07Z-
dc.date.available2021-08-03T10:09:07Z-
dc.date.issued2020-
dc.identifier.citationRobic, F., Micallef, D., Borg, S. P., & Ellul, B. (2020). Implementation and fine-tuning of the Big Bang-Big Crunch optimisation method for use in passive building design. Building and Environment, 173, 106731.en_GB
dc.identifier.urihttps://www.um.edu.mt/library/oar/handle/123456789/79233-
dc.description.abstractPassive building design involves consideration of a number of design variables which can be optimised with respect to some design objective. The use of optimisation methods in building design is well established but the implementation of the Big Bang-Big Crunch algorithm to passive building design is still as yet unexplored in detail. The aim of this paper is to demonstrate the usefulness of the method as well as to fine-tune its characteristic parameters for its efficient use in building design applications. Two building design scenarios are used to have more than one test case and the number of design variables are limited to an extent that an enumeration search can be performed in a reasonable time frame from which the optimum can be precisely established. The Big Bang-Big Crunch algorithm is used and validated against the true solution from the enumeration search. Results show that the α and β parameters should be set to 0 and around 0.8 respectively. The ratio of the sample size to the number of design variables should ideally be between 2.6 and 2.9 for the algorithm to remain efficient and at the same time successful. The paper also discusses the computational efficiency gain of the Big Bang-Big Crunch algorithm compared to a full enumeration search. Computational savings of more than 90% are possible.en_GB
dc.language.isoenen_GB
dc.publisherElsevier BVen_GB
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_GB
dc.subjectSustainable buildings -- Design and constructionen_GB
dc.subjectArtificial intelligenceen_GB
dc.subjectAlgorithmsen_GB
dc.subjectBuildings -- Environmental engineeringen_GB
dc.subjectHeatingen_GB
dc.titleImplementation and fine-tuning of the Big Bang-Big Crunch optimisation method for use in passive building designen_GB
dc.typearticleen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.description.reviewedpeer-revieweden_GB
dc.identifier.doi10.1016/j.buildenv.2020.106731-
dc.publication.titleBuilding and Environmenten_GB
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