Does architectural design require single-objective or multi-objective optimisation? A critical choice with a comparative study between model-based algorithms and genetic algorithms

Efficiency and accuracy have been challenging in the design optimisation process driven by building simulation. The literature review identified the limitations of previous studies, prompting this study to explore the performance of single-objective versus multi-objective efficiency and accuracy on equivalent problems based on control variables and to consider more algorithmic options for a broader range of designs. This study constructed a comparative energy-related experiment whose results are in the same unit, either as a single-objective optimisation or split into two objectives. The project aims to reduce annual energy consumption and increase solar utilisation potential. Our approach focuses on the use of a surrogate modelling algorithm, Radial Basis Function Optimisation Algorithm (RBFOpt), with its multi-objective version RBFMOpt, to optimise the energy performance while quickly identifying new energy requirements for an iterative office building design logic, contrast to traditional genetic-algorithm-driven. In addition, the research also conducted a comparative study between RBFOpt and Covariance Matrix Adaptation Evolutionary Strategies (CMAES) in a single-objective comparison and between RBFMOpt and Nondominated Sorting Genetic Algorithm II (NSGA-II) in a multi-objective optimisation process. The comparison of these sets of Opt algorithms with evolutionary algorithms helps to provide data-driven evidence to support early design decisions.