MULTI-OBJECTIVE OPTIMIZATION OF HYDRODYNAMIC BEARING BASED ON ADAPTIVE GOLDEN JACKAL ALGORITHM (MT) - XU Kai; ZHANG HuiFang (Jixie qiangdu)
In order to improve the load-carrying capacity of the hydrodynamic bearing, and reduce the calorific value and friction coefficient at the same time, a multi-objective optimization model of the hydrodynamic bearing was established. Aiming at the shortcomings of the traditional optimization algorithm that the convergence speed is slow and it is easy to fall into the local optimal solution, an adaptive strategy assisted the golden jackal optimization algorithm is proposed to improve the exploration and exploration ability of the golden jackal algorithm. The performance of the adaptive strategy assisted the golden jackal optimization algorithm is verified by an example with constraints. The results show that the adaptive strategy assisted the golden jackal optimization algorithm has good convergence performance. The algorithm is used to solve the multi-objective optimization problem of hydrodynamic bearings. The optimization results show that the optimized structure has a great improvement in performance compared with the initial structure, the load capacity is increased by 12.257%, and the calorific value and friction coefficient are reduced respectively by 15.610% and 33.333%.