新型动态粒子群算法及其在工业控制中的应用

粒子群优化算法是一种在复杂优化问题的空间域探求最优解的启发式搜索方法。为了优化传统的PSO算法,缩短其运行时间,提出了一种动态收缩型的粒子群优化。收缩型粒子群算法在初始阶段含大量的粒子,随着迭代次数的增加,粒子数量不断减少,仿真结果显示,此方法相比于传统的PSO算法可减少近60%的运算时间。运用此改进的粒子群算法优化理论,实现了对PID控制参数的自适应调节。结果表明,新型的PSO算法可以使得PID控制参数调整速度更快,产生超调量小。

Application of Evolutionary Dynamic PSO in Industry Control

Particle Swarm Optimization（PSO） is a heuristic search method for the exploration of solution spaces of complex optimization problems.With the aim of reducing execution time,a dynamic population size improvement for classical PSO is explored.Diminishing Population PSO（DP-PSO） starts with a large number of particles and iteratively reduces the swarm size.The simulation result shows this improvement produce almost 60% reduction in the execution time compared to the classical PSO.The evolutionary optimization theory of Particle Swarm Algorithm is automatically applied to tune the parameters of PID controllers.The result indicates that the new-style PSO makes the tuning of PID controller parameters more quickly,and the overshoot small.