Qiqi Liu, Felix Lanfermann, Tobias Rodemann, Markus Olhofer, Yaochu Jin, "Surrogate-Assisted Many-objective Optimization of Building Energy Management", IEEE Computational Intelligence Magazine, vol. 18, no. 4, pp. 14-28, 2023.

Building energy management usually involves a number of objectives such as investment costs, thermal comfort, system resilience, battery life, and many others. However, most existing studies merely consider optimizing less than three objectives since it becomes increasingly difficult as the number of objective increases. In addition, the optimization of building energy management heavily relies on time-consuming energy simulators, posing great challenges for conventional evolutionary algorithms that typically require a large number of real function evaluations. To address the above issues, this paper formulates the building energy management as a ten-objective optimization problem, aiming to finding optimal configurations of power supply components. To solve this expensive many- objective optimization problem, we compare five state-of-the-art multi-objective evolutionary algorithms, four of which are assisted by surrogate models. Experimental results show that the adaptive reference vector assisted algorithm is proven to be the most competitive one among the five compared algorithms; the four evolutionary algorithms with surrogate assistance always outperform their counterpart without the surrogate, although the kriging-assisted reference vector assisted evolutionary algorithm only perform slightly better than the algorithm without surrogate assistance in dealing with the ten-objective building energy management problem. By analysing the non-dominated solutions obtained by the five algorithms, an optimal configuration of power supply components can be obtained within an affordable period of time, providing decision makers new insights into the building energy management problem.