Iterative Discrete Particle Swarm Optimization Algorithm and Its Application to Batch Process Optimization
Ganping Li, Qingnian Wang · 2009
To solve dynamic optimization problems of batch processes without state independent and end-point constraints, an iterative discrete particle swarm optimization (IDPSO) algorithm was developed. The main idea of the algorithm was to execute the discrete particle swarm optimization (DPSO) iteratively then the control profile would converge to an optimal one. For the method, the control region and time interval were discretized to a finite number of decision variables and DPSO was then used to search for the best control vector. The searching space contracted as iterations proceeded hence the performance index and control profile could achieve the best value. The results of each iterated calculation were filtered by a three-point linear smooth operator, which makes the optimal trajectory smooth and steady. The simulation result of a batch process shows that the IDPSO algorithm can solve the dynamic optimization problems effectively if there is no state independent and end-point constraints.