【作者】 李猛； 王道波； 甄子洋；

【Author】 Meng Li,Daobo Wang,Ziyang Zhen College of Automation Engineering,Nanjing University of Astronautics and Aeronautics,Nanjing,210016

【机构】 南京航空航天大学自动化学院；

【摘要】 针对粒子群算法早熟收敛、优化后期收敛速度慢的缺点,本文将BOIDS鸟群模型的避免碰撞机制融入到粒子群算法中,并与Powell算法相结合,提出了一种能够有效加快收敛速度和优化精度的改进混合粒子群优化算法。将该算法用于SISO系统模型降阶问题,为了降低优化问题的维数,将降阶模型传递函数的分子系数表示成分母系数的最小二乘解。对典型模型降阶问题的仿真结果表明所提出方法的可行性和有效性。更多还原

【Abstract】 An improved hybrid particle swarm optimization algorithm(IHPSO) is proposed to deal with the problem of premature convergence and slow search speed in particle swarm optimization algorithm(PSO).New algorithm makes use of the principle of collision avoidance in BOIDS birds model and combine with Powell algorithm.This new algorithm is used to solve the model reduction problem in SISO system.In order to reduce the dimension of optimization, the numerator parameters are calculated by the least squares for each of candidates of the denominators parameters. Simulations based on benchmarks show the feasibility and effectiveness of the proposed method.更多还原