Efficient wave energy converter optimisation via control co-design: A comparison of AI-based algorithms

The transition to a sustainable, carbon-neutral global energy system requires a significant expansion of renewable energy sources. Among renewable energy technologies, offshore renewable energy (ORE) systems, including wave energy converters (WECs), offer promising alternatives to traditional fossil fuel-based energy generation. Lately, control co-design (CCD) techniques are becoming more popular to boost WECs commercial viability, but they present complex optimisation challenges due to the large amount of variables to be optimised. In this study, four global optimisation techniques—genetic algorithms (pattern search, particle swarm optimisation, and surrogate optimisation) are compared for WEC CCD applications. Through a case study of a two-degree-of-freedom cylindrical WEC, the convergence speed, robustness, and effectiveness of each technique in finding optimal solutions for WEC design and control are assessed. Results demonstrate that all four optimisation techniques converge to similar results, in a fraction of the time required by brute-force methods. Moreover, trajectory-based optimisation strategies, such as pattern search and surrogate optimisation, show promise for efficiently navigating the solution space and avoiding local minima.