迭代粒子群算法及其在间歇过程鲁棒优化中的应用

针对无状态独立约束和终端约束的间歇过程鲁棒优化问题，将迭代方法与粒子群优化算法相结合，提出了迭代粒子群算法．对于该算法，首先将控制变量离散化，用标准粒子群优化算法搜索离散控制变量的最优解．然后在随后的迭代过程中将基准移到刚解得的最优值处，同时收缩控制变量的搜索域，使优化性能指标和控制轨线在迭代过程中不断趋于最优解．算法简洁、可行、高效，避免了求解大规模微分方程组的问题．对一个间歇过程的仿真结果证明了迭代粒子群算法可以有效地解决无状态独立约束和终端约束的间歇过程鲁棒优化问题．

Iterative Particle Swarm Algorithm and Its Application to Robust Optimization of Batch Process

An iterative particle swarm algorithm is proposed for the robust optimization problem of batch process-es without state independent and end-point constraints,which combines the iteration method and the particle swarm optimization algorithm together.For the algorithm,the control variables are discretized firstly and the standard particle swarm optimization algorithm is used to search for the best solution of the discretized control variables.Second,the benchmark is moved to the acquired optimal values in the subsequent iterations and the searching space gets contracted at the same time;hence the optimization performance index and control profile can achieve the best value gradually through iterations.The algorithm is simple,feasible and efficient,and avoids the problem of solving large-scale differential equation group.The simulation results of a batch process shows that the iterative particle swarm algorithm can solve the robust optimization problems of batch processes effectively if there is no state independent and end-point constraints.