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Uncertainty exists in many of the design variables and system parameters for planing craft. This is especially true in the early stages of design. For this reason, and others, optimization of a craft's performance characteristics is often delayed until later in the design process, after uncertainties have been at least partially resolved. However, delaying optimization can also limit its potential, because freedom to make changes to a design is also highly limited in the later stages. This article demonstrates how uncertainty can be directly incorporated into optimization using particle swarm. A simple synthesis model for a planing craft is built, and a deterministic Pareto front of optimal solutions is found, minimizing two objectives: drag and vertical acceleration at the center of gravity. The craft's weight is then modeled as a normally distributed random variable and sampling methods are used to quantify the uncertainty in the estimated drag for points along the Pareto front. Preliminary results reveal that drag uncertainty is not constant along the Pareto front, presenting useful tradeoff information for designers and decision-makers.

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