A preference-based multi-objective building performance optimization method for early design stage

A large number of cases show that the multi-objective optimization method can significantly improve building performance. The method for multi-objective building performance optimization (BPO) design has achieved rapid development in recent years. However, the BPO method still needs to be improved. Specifically, weak interaction between the optimization process and the decision-making process results in low optimization efficiency, which limits the widespread application of the optimization method in early design stage. In this paper, a new interactive BPO mode is explored to strengthen the interaction between the optimization process and decision-making process, and a preference-based multi-objective BPO method is proposed to account for designers’ decision preferences during the optimization process, making the objective more controllable, improving the optimization efficiency and ensuring the diversity of solutions. Firstly, this paper illustrates the proposed method in detail, defines the concept of performance preference, expounds the flow of the preference-based multi-objective optimization algorithm, and proposes three indicators to evaluate the algorithm, which includes convergence speed, preference satisfaction rate, and diversity measurement. Secondly, through testing and comparison, it is found that the proposed preference-based algorithm has advantages over the non-preference optimization algorithm (represented by the NSGA-II algorithm). The proposed method leads to faster convergence and higher preference satisfaction, so it is more suitable for the BPO process in the early design stage. Specially, the proposed method can achieve 100% preference satisfaction rate with only 2400 simulations, while the non-preference method can only achieve 20% preference satisfaction rate after 5800 simulations. In this paper, a preference-based multi-objective BPO method is proposed to make the optimization process closely interact with the decision-making process and make the design preferences be accounted during the BPO process, thereby improving the optimization efficiency. In addition, this study first proposes two indicators to measure the quality of optimization results: preference satisfaction rate and diversity measurement. This study aims to guide the development of BPO methods towards providing high satisfaction rate and high quality optimization results.