| ABSTRACT |
Parameter optimization is important to all technical system in order to achieve a certain degree of output precision or goal. The involvement of multi-resolution parameter makes the process of parameter optimization tedious. This work develops a layered encoding cascade optimization (LECO) model for optimizing multi-resolution parameters using genetic algorithm (GA) and particle swarm optimization (PSO) techniques. In short, the model is called GA-PSO LECO. The developed model is applied to a famous plant-wide industrial control problem, so called Tennessee Eastman (TE) chemical process which involves many large scale, continuous , and nonlinear process.
|