Decision-making based on network visualization applied to building life cycle optimization

We present a building design optimization methodology that has been developed to address issues that researchers and engineers are currently facing when addressing the life cycle optimization of Nearly Zero Energy Buildings (NZEBs). In order to reduce the required computational time, a Kriging model is used to surrogate NZEB performance criteria during the optimization process. The error estimation of the Kriging model is used for an adaptive sequential design to improve the Kriging model accuracy. Α genetic algorithm (NSGA-II) is considered efficient to find the global optimal solutions. We also propose a new algorithm to reduce the calculation time even further. The new individuals of the adaptive sequential design are filtered with satisfaction functions. It means that only the useful part of the Pareto front will be determined. Finally, we use network visualization for decision-making. We show that this approach is very powerful to help designers find one solution in the context of multi-objective optimization. Moreover, the partitions can give useful information about the characteristics of the optimal solutions.