Research on optimization design of UAV main propulsion motor based on Particle Swarm Optimization algorithm

UAV main propulsion motor is required the small mass and Colliers energy density (torque density and power density), so it needs more optimal motor to achieve the purpose. The constraint condition of the main propulsion motor is a complex nonlinear continuous function, and the general Particle Swarm Optimization (PSO) algorithm is used to solve the problem when the multiple objectives are converted into different weights, and then converted to a single target for processing. And the weight of different targets is given based on experience, but the weight of the new motor is often difficult to determine the weight. In this paper, a new particle swarm optimization algorithm is proposed, which can optimize the two sub populations, and then exchange the optimal solutions of the two subgroups. Experiments show that this algorithm is applicable to main propulsion motor quality and efficiency of optimization, and the optimization results and general algorithm are compared. It is proved that the algorithm has high accuracy and fast convergence speed and is suitable for solving the similar problems.