A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency

Most common practices for solving building retrofit problems lack efficiency and overall robustness. Knowledge of novel methods that support decision-making (DM) for retrofitting is critical for sustainability and energy performance improvement. This systematic review for the first time provides a large evidence-base to assess the potential of Multi-objective optimisation (MOO) using Genetic algorithm (GA) for supporting the development of retrofitting strategies and its DM process. From 557 screened studies, 57 were reviewed focusing on outcomes, current trends, and the method's potential, challenges, and limitations. Key findings reveal a strong suitability for solving a wide range of building retrofit MOO problems, based on robust outcomes with significant objectives improvement. However, results also indicate that yielding optimal retrofit solutions may require GA-mixed techniques or modified GA, due to time-consuming and effectiveness issues. Heritage buildings, where qualitative objective function definition is particularly challenging, have been little addressed. Further challenges include: lack of standard systematic approach; complex switch between modelling and optimisation environment; high expertise needed to perform MOO and manage software; and lack of confidence in results. While GA-based MOO's robust evaluation for supporting building retrofit and its DM process needs further research, promising potential is shown overall, when complemented with auxiliary techniques. (C) 2019 Elsevier B.V. All rights reserved.