Marine Propeller Design Using Evolving Chaotic Autonomous Particle Swarm Optimization

Due to many antithetical design parameters and complex fluctuating underwater conditions, marine propeller design has been one of the researchers' challenging problems. Recently, meta-heuristic algorithms have become highly efficient solutions for solving complex engineering problems. However, due to the meta-heuristic algorithm's stochastic nature, they are not reliable for industrial applications such as marine propeller design. Therefore, for the sake of having a robust meta-heuristic optimizer, In this paper, the conventional Particle Swarm Optimization (PSO) algorithm is improved by modified chaotic self-governing groups of particles (MGPSO). In order to approve the efficiency of the designed algorithm, this paper first investigates MGPSO's performance on six challenging benchmark functions. Then, the MGPSO is used to design the marine propellers optimally. To this aim, two targets, viz., maximize the propeller efficiency and minimize its cavitation, which conflicts with each other, are considered the fitness function. In this regard, the propeller's chord length and thickness are considered two main design parameters. The adverse effects of uncertainties in design parameters and operating conditions on efficiency and cavitation also are considered. In this regard, MGPSO is evaluated against the recently proposed benchmark algorithms such as ALO and BBO. The results indicated that MGPSO could find an exact true Pareto optimal front with a uniformly distributed approximation. The results also show that the propeller with 5 or 6 blades with rotation speeds between 180 to 190 RPM will have the best performance in the trade-off between efficiency and cavitation.