A Comparison of Three Evolutionary Algorithms for the Optimization of Building Design

Abstract

In building design, the need of optimization algorithms is considerably increasing due to the requirements of enhancing the overall performances, cost and sustainability objectives. An evolutionary algorithm coupled with building simulation code is often used. However, this approach is not widespread in actual application due to the high number of expensive simulation runs required by evolutionary algorithms. For this reason, the selection of an efficient and effective optimization algorithm becomes a key aspect in building design. In the literature there are several works analyzing the performance of different optimization algorithms, most of them by comparing the results obtained for the optimization of analytical test functions. However, there are no evidence-based studies deepening the efficiency and efficacy of these methods by comparing against the true solution of the discrete optimization problems on building design. This study compares the number of cost function evaluations and the percentage of the actual Pareto solutions of three algorithms used for the evaluation of the optimal refurbishment of three reference buildings for which the actual Pareto front is known through a brute-force approach.