Immune Particle Swarm Optimization Algorithm for Turbo-fan Engine Performance Simulation

Turbo fan engine mathematical model is a highly complex nonlinear system. Solving engine mathematical model with traditional iteration methods turns out to be difficult as these methods are very sensitive to initial values and inclined to divergence. Therefore particle swarm optimization is used to solve the model. To solve the local convergence problem of PSO, some mechanisms in immune algorithm are introduced. Clone selection mechanism based on Logistic chaotic mutation and diversity maintaining mechanism based on probability are designed. Results on test nonlinear equations show that the proposed algorithm has better searching performance and convergence speed than other compared algorithms. And ideal results are obtained with new algorithm when modeling a mixed exhaust turbofan engine.