Multi-Objective Optimum Design of Rotor-Bearing Systems With Dynamic Constraints Using Immune-Genetic Algorithm
Abstract An immune system has powerful abilities such as memory, recognition and learning how to respond to invading antigens, and is applying to many engineering algorithms in recent year. In this paper, the combined optimization algorithm (Immune-Genetic Algorithm: IGA) is proposed for multi-optimization problems by introducing the capability of the immune system that controls the proliferation of clones to the genetic algorithm. The new combined algorithm is applied to minimize the total weight of the shaft and the resonance response (Q factor), and to yield the critical speeds as far from the operating speed as possible. These factors play very important roles in designing a rotor-bearing system under the dynamic behavior constraints. The shaft diameter, the bearing length and clearance are chosen as the design variables. The dynamic characteristics are determined by applying the generalized FEM. The results show that the combined algorithm can reduce the weight of the shaft and improve the critical speed and Q factor with dynamic constraints.