Multi-objective optimization of a semi-submersible floating wind Platform: Integrating cost, stability, and strength

Abstrac t The economic viability of floating offshore wind turbines (FOWTs) is still limited by the high construction cost and structural complexity of their supporting platforms, which can account for over 40 % of total capital expenditure in current commercial projects. This study proposes a unified, simulation-driven optimization framework that concurrently minimizes material-equivalent cost, pitch motion, and structural stress through parametric design and multi-objective genetic optimization. These three objectives are formulated to simultaneously address economic feasibility, motion stability, and the internal stress distribution that governs material usage and structural reinforcement demands. The framework integrates geometry generation, hydrodynamic analysis, and finite-element stress evaluation within a Python-based automation loop, enabling rapid, iterative evaluation across disciplines. Compared to the initial design, the optimized platform reduces structural mass by 14.7 %, pitch amplitude by 78.7 %, and peak stress by 43.2 %. Additionally, when benchmarked against the OO-Star platform, it achieves a 21.2 % lower mass-to-power ratio and an LCOE reduction of $11.2/MWh. Validation through hydrodynamic analysis simulations under multiple wave headings confirms the effectiveness of the design in suppressing motion and improving load distribution. The results demonstrate that integrated multi-objective optimization provides a practical and effective means of balancing cost, stability, and structural safety in the early-stage design of FOWT platforms.