Abstract: PID controller’s performance completely depends on the combination of the control parameters，an improved particle swarm optimization（Crossbreed-PSO） is proposed in tuning and optimization of PID parameters in this paper，it adopts the crossbreeding mechanism in genetic algorithm，and using non-linear inertia weight reduction strategy to accelerate the optimize convergence and improve the search capabilities of particles.The algorithm is applied to optimize a second-order system PID control parameters，simulation results show that the improved PSO has a better performance than both the conventional particle swarm optimization and genetic algorithm，which means a well prospective.

摘要： PID控制器的性能取决于其控制参数的组合，针对其参数的整定和优化问题，提出了应用一种改进的粒子群优化算法，该算法借鉴了遗传算法的杂交机制，并采用惯性权值的非线性递减策略，用以加速算法的收敛速度和提高粒子的搜索能力。将该算法应用于一个二阶系统的PID控制器参数的优化。仿真结果表明该改进的粒子群算法具有比传统粒子群算法和遗传算法更好的优化效果，具有一定的工程应用前景。