Abstract
While simulation-based optimization can effectively find good solutions, the need to simulate hundreds of candidates and consequent long run-times prevent their application in practice. Accurate and fast surrogate models can replace expensive building performance simulations (BPS). Model-based optimization algorithms construct a surrogate during optimization and perform many additional optimization steps quickly. While this strategy has proven effective for expensive single-objective optimization, its performance on multi-objective BPS problems remains understudied. Two questions persist: A) Do model-based multi-objective optimization algorithms outperform metaheuristics and B) How does optimizing on a surrogate model affect the performance of metaheuristic optimization algorithms? Our benchmark results show that the model-based algorithms RBFMOpt and TPE outperform metaheuristics regarding robustness and the quality of the found Pareto fronts. RBFMOpt yields good solutions within less than $100$ function evaluations. Optimizing with metaheuristics on surrogate models heavily depends on the surrogates’ ability to estimate precisely and cannot circumvent metaheuristics’ shortcomings in robustness.
Users
This publication ist of type "preprint". To see comments and reviews from other users, you have to create your own comment or review for this post first.
Please
log in to take part in the discussion (add own reviews or comments).
