Improved Particle Swarm Algorithm Based Multi-Objective Optimization of Diaphragm Spring of the Clutch

Considering that diaphragm spring is the core com-ponent of the mechanical clutch, the optimization to which plays practical roles in engineering practices, the multi -ob-jective optimization model for the diaphragm spring of the clutch is established in this article. Aiming at the difficulty in local extremum due to pre-maturity of inertia weight and treatment on nonlinear constraint condition of standard par-ticle swarm optimization (PSO), the improved particle swarm algorithm (Improved PSO) based on dynamic weight and hierarchical penalty function in consideration of the de-gree of congestion is proposed in this article to improve the original particle swarm algorithm. According to the results of calculating examples, the improved particle swarm algo-rithm can achieve better global searching ability and con-vergence ability; when compared with the calculating re-sults of the penalty function algorithm, the genetic algorithm and the NSGA-II algorithm, the pressing force of the dia-phragm spring with the new algorithm is increased by 3.24%, and the steering separation force is decreased by 20.09%. The diaphragm spring has better pressing force sta-bility and operating lightness, verifying the correctness of the model and the algorithm proposed in this article.