Optimizing an expensive multi-objective building performance problem: Benchmarking model-based optimization algorithms against metaheuristics with and without surrogates

•Popular multi-objective metaheuristic optimization algorithms are benchmarked against state-of-the-art model-based optimizers.•Benchmark is conducted on a real-world building performance simulation problem.•Model-based optimization outperforms metaheuristics both in terms of robustness and achieved...

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Vydáno v:Energy and buildings Ročník 336; s. 115562
Hlavní autoři: Zorn, Max, Claus, Luisa, Frenzel, Christian, Wortmann, Thomas
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.06.2025
Témata:
Architectural design optimization
Benchmark
Model-based optimization
Multi-objective optimization
Surrogate models
Benchmark
Multi-objective optimization
Surrogate models
Model-based optimization
Architectural design optimization
ISSN:0378-7788
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Popis
Shrnutí:•Popular multi-objective metaheuristic optimization algorithms are benchmarked against state-of-the-art model-based optimizers.•Benchmark is conducted on a real-world building performance simulation problem.•Model-based optimization outperforms metaheuristics both in terms of robustness and achieved hypervolume.•Using machine learning surrogates speeds up metaheuristic optimization but does not outperform model-based optimizers. [Display omitted] 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, maximum hypervolume, and the quality of the found Pareto fronts. RBFMOpt yields good solutions within less than 100 function evaluations. Optimizing on surrogate models heavily depends on the surrogates’ ability to estimate precisely but is computationally cheap and allows larger optimization budgets.
ISSN:0378-7788
DOI:10.1016/j.enbuild.2025.115562