  Typical parametric approaches restrict the exploration of diverse designs by
generating variations based on a baseline design. In contrast, generative
models provide a solution by leveraging existing designs to create compact yet
diverse generative design spaces (GDSs). However, the effectiveness of current
exploration methods in complex GDSs, especially in ship hull design, remains
unclear. To that end, we first construct a GDS using a generative adversarial
network, trained on 52,591 designs of various ship types. Next, we constructed
three modes of exploration, random (REM), semi-automated (SAEM) and automated
(AEM), with varying levels of user involvement to explore GDS for novel and
optimised designs. In REM, users manually explore the GDS based on intuition.
In SAEM, both the users and optimiser drive the exploration. The optimiser
focuses on exploring a diverse set of optimised designs, while the user directs
the exploration towards their design preference. AEM uses an optimiser to
search for the global optimum based on design performance. Our results revealed
that REM generates the most diverse designs, followed by SAEM and AEM. However,
the SAEM and AEM produce better-performing designs. Specifically, SAEM is the
most effective in exploring designs with a high trade-off between novelty and
performance. In conclusion, our study highlights the need for innovative
exploration approaches to fully harness the potential of GDS in design
optimisation.
