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This thesis contains interrelated studies aimed at increasing annual energy produc-tion and enhancing the hydrodynamic interaction within hybrid wind–wave energy converter systems. The first stage investigates how the mass distribution and posi-tion of a wave absorber can be adjusted to enable passive motion control, thereby improving wave energy capture. Following this, a geometric optimization framework is developed for a semi-submersible platform, employing genetic algorithm to identify design parameters that maximize power generation by optimizing the relative motion between the platform and integrated wave absorbers. The research further emphasizes the importance of reliable wave absorber models, demonstrating how robust forecast-ing, using machine learning, methods can be applied to adapt the system for varied oceanic conditions. The study extends the optimization framework to a multi-source offshore renewable energy park that includes wind turbines, floating photovoltaics, and wave converters. A permutation-based aggregator logic, inspired by a 3–8 line decoder and optimized using a genetic algorithm, allows for partial or full curtailment of individual energy sources in discrete steps. This strategy minimizes energy losses at the point of common coupling and balances the capacity factor. Finally, the study examines the impact of the wind turbine’s aerodynamic forces on the performance of the wave absorbers, revealing that steady wind conditions enhance wave energy capture, while turbulent wind introduces variability in absorber motion, slightly re-ducing efficiency. Collectively, the findings show an integrated approach, combining analytical models, numerical simulations, and advanced optimization techniques, that can substantially improve wave energy extraction, system stability, and overall annual energy yield.