The present study aims to carry out a comparative Multi-Objective Optimization (MOO) of a 10 MW FOWT semi-submersible using three different metaheuristic optimization techniques and a sophisticated approach for optimizing a floating platform. This novel framework enables highly efficient 3D plots, an optimization loop, and the automatic and comparative output of solutions. Python, the main interface, integrated PyMAPDL and Pymoo for intricate modeling and simulation tasks. For this case study, the ZJUS10 Floating Offshore Wind Turbine (FOWT) platform, developed by the state key laboratory of mechatronics and fluid power at Zhejiang University, was employed as the basis. Key criteria such as platform stability, overall structural mass, and stress were pivotal in formulating the objective functions. Based on a preliminary study, the three metaheuristic optimization algorithms chosen for optimization were Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Ant Colony Optimization (ACO). Then, the solutions were evaluated based on Pareto dominance, leading to a Pareto front, a curve that represents the best possible trade-offs among the objectives. Each algorithm’s convergence was meticulously evaluated, leading to the selection of the optimal design solution. The results evaluated in simulations elucidate the strengths and limitations of each optimization method, providing valuable insights into their efficacy for complex engineering design challenges. In the post-processing phase, the performances of the optimized FOWT platforms were thoroughly compared both among themselves and with the original model, resulting in validation. Finally, the ACO algorithm delivered a highly effective solution within the framework, achieving reductions of 19.8% in weight, 40.1% in pitch, and 12.7% in stress relative to the original model.

Blades have a significant influence on a tidal current turbine (TCT)’s energy transfer capacity. In order to ensure a blade working safely and efficiently in low flow rates, a special design is needed. Hydrofoil’s shape and blade’s quality are the key factors which affect blade’s dynamic and starting performance at low flow rate. In this research, basing on a 500 W_ORI blade (an initial design of 500 W tidal turbine’s blade), optimizations of hydrofoil and blade structure were done sequentially by genetic algorithm. After hydrofoil optimization we got optimized hydrofoil OPT_24 which has high dynamic performance, good startup performance and durable structure, then redesigned the shape of new 500 W_OPT blade (a 500 W tidal turbine’s blade using optimized hydrofoil). After that, the structure of 500 W_OPT blade was designed empirically and optimized to reduce its weight. Both 500 W_ORI and 500 W_OPT were fabricated, and their efficiency and the structure strength were tested. The experimental results show that the strain of the 500 W_OPT blade is smaller than that of the 500 W_ORI blade under the same load. The power efficiency of the 500 W_OPT at the optimal tip speed ratio is increased 500 W_ORI by 46.1%, and 500 W_OPT was able to start at the flow rate of 0.5–0.6 m/s.