Abstract:

Hybrid particle swarm optimization was presented to improve the optimizing efficiency of the particle swarm by changing the optimizing strategy of the global best particle. Aimed at the problem of optimization with a limit on computing time, such as the state prediction of a typical equipment in process industry, hybrid particle swarm optimization took the global best position found by the particle swarm as a special particle, which performed the gradient descending optimization. By adding the individual gradient descending optimization of the global best particle to the optimization iterations, the global search and local search were combined in hybrid particle swarm optimization. The hybridism of this new particle swarm optimization improved the optimizing efficiency of the particle swarm, and reduced the time of optimization computing. In the test of a real application, hybrid particle swarm optimization was applied to the state prediction of the continuous stirred tank reactor (CSTR), which is a typical equipment of the process industry. In the test training of neural network that was used in the prediction of the concentration of the CSTR product, hybrid particle swarm optimization took less optimizing iterations than the traditional particle swarm optimization, and took less optimization computing time, which showed that hybrid particle swarm optimization could reduce the computing time of optimization as the original intent of this research.