... front, which was generated by applying the NSGA-II genetic algorithm in MATLAB to identify the set of optimal, non-dominated solutions. Findings The optimization yielded a Pareto front of optimal solutions. By prioritizing solutions closest to this frontier, four optimal scenarios (T1–T4) were...

... of this study exploits the non-dominated sorting genetic algorithm (NSGA-II) optimization algorithm coupled with building simulation to research a trade-off between the optimization of investment cost and energy consumption. Our approach focuses on the whole optimization problem of thermal envelope, glazing...

... consumption. Regarding the approach for determining the optimum values, the non-dominated sorting genetic algorithm II (NSGA-II) is applied to a range of values obtained from field measurements. The proposed prediction method and approach for determining optimum values were tested using data of energy...

... consumption, natural lighting and natural ventilation. Genetic algorithm was used to optimize the building parameters to achieve energy conservation and comfort improvement. Then a two-story rental building was analyzed. Findings The genetic algorithm converged to Pareto optimal solution set after 10,000...