Proefschrift

Multi-objective mixed-integer evolutionary algorithms for building spatial design

Multi-objective evolutionary computation aims to find high quality (Pareto optimal) solutions that represent the trade-off between multiple objectives.

Auteur

Blom, K. van der

Datum

11 december 2019

Links

Thesis in Leiden Repository

Multi-objective evolutionary computation aims to find high quality (Pareto optimal) solutions that represent the trade-off between multiple objectives. Within this field there are a number of key challenges. Among others, this includes constraint handling and the exploration of mixed-integer search spaces. This thesis investigates how these challenges can be handled at the same time, and in particular how they can be applied in the multi-objective optimisation algorithms. These algorithms are developed in the context of the optimisation of building spatial designs, which describe the exterior shape of a building, and the internal division into different spaces. Spatial designs are developed early in the design process, and thus have a large impact on the final building design, and in turn also on the quality of the building. Here the structural and thermal performance of a building are optimised to reduce resource consumption. The main contributions of this thesis are as follows. Firstly, a representation for building spatial designs in is introduced. Secondly, specialised search operators are designed to ensure only feasible solutions will be explored. Thirdly, data about the discovered solutions is analysed to explain the results to domain experts. Finally, a general purpose multi-objective mixed-integer evolutionary algorithm is developed.

• building physics
• building spatial design
• constraint handling
• evolutionary algorithms
• evolutionary multicriterion optimisation
• evolutionary optimisation
• mixed-integer optimisation
• multi-objective optimisation
• multicriteria optimisation
• structural design