中文摘要：

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。

英文摘要：

Aiming at the problem that the particle swarm optimization（PSO） has lower local search capability, an improved PSO based on harmony search（ HS-PSO）is proposed to tune the PID controller parameters. Population schedule factor is adopted to self-tune the inertia weight in order to enhance the convergence speed of PSO algorithm. The hybrid optimization algorithm storesthe best individual produced in each generation of PSO evolution process into the harmony memory with the metabolic manner and carries through the harmony search in order to overcome the shortcoming of lower local search. It is demonstrated by numerical simulations on the classical objects to tune the parameters of the PID controller that the processed HS-PSO algorithm has the more excellent global optimization performance than other intelligent optimization algorithms.