Amphibious vehicle's resistance optimization through neural networks and genetic algorithms

Amphibious vehicles, as a new type of aquatic and terrestrial transport platform, are increasingly involved in the existing transportation system. Resistance is a key factor that affects the efficiency and energy consumption of vehicles in aquatic sailing. Resistance reduction optimization design is a focal point and challenge in the design process of amphibious vehicles. In this paper, a resistance performance optimization method has been proposed based on neural networks and genetic algorithms. First, key parameters for the shape design are extracted based on a thorough understanding of the vehicle's performance. These parameters are used to construct a parameterized design space. Second, a training set is obtained based on the Latin hypercube sampling method and numerical calculation methods, and a test set is randomly generated. To achieve better resistance prediction performance, a method based on the genetic algorithm-optimized backpropagation Neural Network is proposed. Next, the resistance performance of the two operating conditions is optimized through the non-dominated sorting genetic algorithm II, and optimized configuration parameters are obtained, which has a 22.71% energy-saving ratio at cruising speed. Finally, the optimized configuration is analyzed using numerical calculation methods to validate the resistance prediction and optimization methods.