Iterative ant-colony algorithm and its application to dynamic optimization of chemical process

For solving dynamic optimization problems of chemical process with numerical methods, a novel algorithm named iterative ant-colony algorithm (IACA), the main idea of which was to iteratively execute ant-colony algorithm and gradually approximate the optimal control profile, was developed in this paper. The first step of IACA was to discretize time interval and control region to make the continuous dynamic optimization problem be a discrete problem. Ant-colony algorithm was then used to seek the best control profile of the discrete dynamic system. At last, the iteration based on region reduction strategy was employed to get more accurate results and enhance robustness of this algorithm. Iterative ant-colony algorithm is easy to implement. The results of the case studies demonstrated the feasibility and robustness of this novel method. IACA approach can be regarded a,,; a reliable and useful optimization tool when gradient is not available. (c) 2005 Published by Elsevier Ltd.