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Efficient Genetic Algorithm sets for optimizing constrained building design problem

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journal contribution
posted on 14.09.2016, 15:56 by Jonathan Wright, Ali F. Alajmi
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.

History

School

  • Architecture, Building and Civil Engineering

Published in

International Journal of Sustainable Built Environment

Volume

5

Issue

1

Pages

123 - 131

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

Publisher

© Elsevier

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

03/04/2016

Publication date

2016

Notes

This is an Open Access paper funded by The Gulf Organisation for Research and Development.

ISSN

2212-6090

Language

en