High-dimensional Bayesian optimization applied to a ship energy system

Abstract

The design of a ship energy system can be complex due to multiple design options (e.g choice for component types or occurence) and constraints (e.g environmental, economical, military). The optimization problem that can help to find an optimal architecture (components and associated connections) is typically referred as a synthesis, design and operation optimization (SDO) problem. Evolutionary algorithms are usually employed to tackle SDO problems and they provide good results. However, due to the very important number of objective function evaluations involved, the physical modeling must remain very simple (causal behavior without differential equations or matrix inversions). A Bayesian optimization (BO) approach is proposed in this article to tackle this issue. The expected advantage of BO is its ability to represent a global approximation of the objective function with few data points while its weakness lies in the number of optimization variables it can handle. This high-dimensional challenge is tackled here by a decomposition of the optimization problem into the different modes that represent the ship mission profile. A numerical example is presented for a ship energy system than can achieve propulsion and electricity production by multiple means. The effect of the formulation of constraints in the objective function will also be discussed as it can have a strong impact on the optimization results.