|dc.description.abstract||Non-dominated sorting genetic algorithm version 2
(NSGA-II) is a multi-objective optimisation method.
NSGA-II is often used to optimise the design of
building. This paper details small improvements to this
algorithm using ‘fitness approximation’ methods.
Fitness approximation is used speed up the conversion of
NSGA-II. Radial basis functions networks have been
shown to be useful for this. Although there are many
types of fitness approximation function that could be use
for this purpose, Kriging methods have not yet been
In this paper, Kriging models are compared to standard
NSGA-II. The results show that Kriging-based fitness
approximation slightly improves upon standard NSGAII.
More work is needed to test this method on different
building types as well as more complex problems, such
as those associated with HVAC design.||en_GB
|dc.description.sponsorship||The authors would like to thank for EPSRC for funding
this research [Ref: EP/M021890/1]||en_GB
|dc.identifier.citation||Building Simulation 2017, 15th International Conference of IBPSA, 7-9 August 2017, San Francisco, USA||en_GB
|dc.publisher||International Building Performance Simulation Association ((IBPSA)||en_GB
|dc.rights||© 2017 IBPSA||en_GB
|dc.title||Using Kriging regression to improve the stability and diversity in NSGA-II||en_GB
|dc.description||This is the author accepted manuscript. The final version is available from IBPSA||en_GB