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针对粒子群算法(PSO)算法局部搜索能力差的问题,提出一种对PID控制器参数进行自整定的基于和声搜索(HS)的改进粒子群优化算法(HS-PSO)。通过引入种群进程因子对惯性权重进行自适应调节以提高PSO算法的收敛速度。另外在PSO进化过程中每代产生的最优个体以新陈代谢方式进入和声记忆库中并进行和声搜索,以克服粒子群优化算法局部搜索能力差的缺陷。针对典型对象进行PID控制器参数自整定,仿真和工程应用结果表明所提HS-PSO算法较他它智能优化算法具有更好的全局优化能力。
Aiming at the poor local search ability of Particle Swarm Optimization (PSO) algorithm, an improved Particle Swarm Optimization (HS-PSO) algorithm based on Harmony Search (HS) for self-tuning PID controller parameters is proposed. The inertia weight is adaptively adjusted by introducing the population process factor to improve the convergence speed of PSO algorithm. In addition, the best individuals generated by each generation in the process of evolution of PSO enter into the harmony memory in the metabolic way and carry out the harmony search, so as to overcome the defect that the particle swarm optimization algorithm has poor local search ability. PID controller parameters self-tuning for typical objects, simulation and engineering application results show that the proposed HS-PSO algorithm has better global optimization than other intelligent optimization algorithms.