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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/22446

Title: Efficient Genetic Algorithm sets for optimizing constrained building design problem
Authors: Wright, Jonathan A.
Alajmi, Ali F.
Keywords: Constrained building optimization problem
Genetic Algorithm (GA)
GA control parameters
Simulation calls
Thermal comfort
Issue Date: 2016
Publisher: © Elsevier
Citation: WRIGHT, J., and ALAJMI, A., 2016. Efficient Genetic Algorithm sets for optimizing constrained building design problem. International Journal of Sustainable Built Environment, 5 (1), pp.123-131
Abstract: The main aim of this paper is to find the appropriate set of Genetic Algorithm (GA), control parameters that attain the optimum, or near optimum solutions, in a reasonable computational time for constrained building optimization problem. Eight different combinations of control parameters of binary coded GA were tested in a hypothetical building problem by changing 80 variables. The results showed that GA performance was insensitive to some GA control parameter values such as crossover probability and mutation rate. However, population size was the most influential control parameter on the GA performance. In particular, the population sizes (15 individuals) require less computational time to reach the optimum solution. In particular, a binary encoded GA with relatively small population sizes can be used to solve constrained building optimization problems within 750 building simulation calls.
Description: This is an Open Access paper funded by The Gulf Organisation for Research and Development.
Version: Published
DOI: 10.1016/j.ijsbe.2016.04.001
URI: https://dspace.lboro.ac.uk/2134/22446
Publisher Link: http://dx.doi.org/10.1016/j.ijsbe.2016.04.001
ISSN: 2212-6090
Appears in Collections:Published Articles (Architecture, Building and Civil Engineering)

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