AGENTIC-SYSTEMSJune 2, 20261 min read7 views

Optimal Transport-based Permutation-Invariant Bayesian Optimization of Offshore Wind Farm Layouts

Researchers have proposed a Permutation-Invariant Bayesian Optimization (PIBO) approach based on Optimal Transport theory to optimize offshore wind farm layouts. This new method not only delivers better energy production layouts but also cuts computation time in half compared to traditional Bayesian Optimization.

Computer Science > Artificial Intelligence

Title:Optimal Transport-based Permutation-Invariant Bayesian Optimization of Offshore Wind Farm Layouts

View PDF HTML (experimental)Abstract:Bayesian Optimization (BO) is widely and successfully adopted for solving optimization problems having an expensive-to-evaluate, black-box, and non-convex objective function. However, the vanilla BO algorithm is not able to exploit possible symmetries characterizing the target problem. An intuitive case is given by optimal location problems, whose decision variables refer to a finite set of points within a continuous space, with the order of points not affecting the value of the objective function. We refer to this setting as optimization over layouts to distinguish from optimization over point-clouds where, instead, the order of points counts. As an instance of optimization over layouts we consider a real-life industrial-relevant application, that is the optimization of the layout of an offshore wind farm: given identical wind turbines, switching any pair of them has not any effect on the annual energy production. Based on Optimal Transport theory, we propose a Permutation-Invariant BO approach, namely PIBO, proved to provide better wind farm layouts when compared to the vanilla BO approach while cutting computation time roughly in half.

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Source: arXiv cs.AI Recent

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