一种自适应混合粒子群优化算法及其应用

Adaptive hybrid particle swarm optimization algorithm and application

为提高粒子群算法的寻优精度,提出一种将单纯形法(SM)与粒子群(PSO)算法相结合的自适应混合粒子群优化(AHPSO)算法。该算法根据进化需要动态调整粒子的惯性权重,并在进化停滞时使用SM优化。通过仿真实验证明了AHPSO的寻优性能优于SPSO和SMPSO。将AHPSO用于某航空发动机的PID参数优化,其整定性能优于现有的工业方法和其他PSO算法。

To improve the performance of the particle swarm optimization（PSO） algorithm,this paper proposed an adaptive hybrid particle swarm optimization（AHPSO） algorithm based on simplex method（SM） and PSO.AHPSO could dynamically adjust the particle＇s inertia weight according to the search needs,used SM to enhance the search capability when meet searching stagnate.The simulations prove the AHPSO have better optimization performance than SPSO and SMPSO.Aero-engine PID controller tuning test prove AHPSO have the better control effect than Ziegler-Nichols,SPSO and SMPSO.