Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation

Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation

Reducing building energy demand is a crucial part ofthe global response to climate change, and evolutionary algorithms (EAs) coupled to building performance simulation (BPS) are an increasingly popular tool for this task. Further uptake of EAs in this industry is hindered by BPS being computationall...

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Bibliographic Details
Main Authors: Alexander E.I. Brownlee, Jonathan Wright
Format: Default Article
Published: 2015
Subjects:
Other built environment and design not elsewhere classified
Artificial intelligence not elsewhere classified
Information systems not elsewhere classified
Simulation-based optimisation
Multi-objective
Constraints
Surrogate
NSGA-II
Information Systems
Built Environment and Design not elsewhere classified
Artificial Intelligence and Image Processing
Online Access:https://hdl.handle.net/2134/18510
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https://hdl.handle.net/2134/18510

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