序贯优化化工动态问题的蚁群算法

针对化工动态优化问题,分析现有数值解法的不足,提出序贯执行蚁群寻优操作,逐步寻找最佳解的策略,构建序贯蚁群算法.算法首先对时间区间和控制变量搜索域实施离散化,以一组整数编码的蚁群路径表示可行控制策略,进而应用蚁群寻优操作寻找离散问题的最优控制策略.逐步收缩控制搜索域并反复上述步骤,不断改善寻优结果.序贯蚁群算法简便快捷,用于化工动态优化问题效果良好,计算结果体现了算法的稳健性.

Sequential Optimization of Chemical Dynamic Problems by Ant-colony Algorithm

After analyzing the demerits of existing numerical methods for dynamic optimization problems of chemical process, a novel method named as sequential ant-colony algorithm (SACA) was developed, in which the main idea is to sequentially perform ant-colony algorithm and gradually attain the optimal control profile step by step. The first step of SACA is to divide time interval and control region to make the continuous dynamic optimization problem becomes discrete problem. Then ant-colony algorithm is used to seek the best control profile of the above system. At last, region-reduction strategy is employed and returns to first step to increases numerical accuracy. Sequential ant-colony algorithm has advantages over existing methods on the performance of efficiency and succinctness. Two examples of using the proposed algorithm to solve the chemical dynamic optimization problems were taken and the results show that the algorithm proposed is feasible and robust.