单纯形微粒群优化算法及其应用

Simplex Particle Swarm Optimization Algorithm and Its Application

将微粒群优化算法(PSO)与单纯形法(SM)相结合,提出单纯形微粒群优化算法(SPSO)。通过对5种常用测试函数进行优化和比较,结果表明SPSO比PSO和SM都更容易找到全局最优解。然后将SPSO用于催化裂化装置主分馏塔粗汽油干点软测量,建立基于SPSO的粗汽油干点神经网络软测量模型,通过与实际工业数据对比,表明该模型具有高的精度、好的性能和广阔的应用前景。

An improved particle swarm optimization algorithm --- simplex particle swarm optimization algorithm （ SPSO） was proposed based on PSO and simplex method （SM）. Then, SPSO, PSO and SM are used to resolve five widely used test functions＇ optimization problems. Results show that SPSO has greater efficiency and better performance than PSO and SM, Next, SPSO is applied to train artificial neural network to construct a practical soft-sensor of gasoline endpoint of main fractionator offluid catalytic cracking unit （FCCU）, The obtained results and comparison with actual industrial data indicate that the new method proposed by this paper is feasible and effective in soft-sensor of gasoline endpoint.