基于自适应差分进化算法的间歇反应动态优化求解

为了求解间歇反应动态优化问题，提出了一种自适应差分进化算法(Self-Adaptive Differential Evolution, SADE)。在SADE算法中，每个个体都拥有自己的控制参数。该算法在对原优化问题进行差分进化搜优的同时，以权重大小来评价各个控制参数的优劣，并以加权控制参数作为控制参数的进化方向，实现其自适应调整。结果表明SADE算法收敛速度快、求解精度高。将SADE算法应用于两个典型的间歇反应动态优化问题中，取得了较好的优化效果；同时，分析了时间离散度对优化结果的影响。

Dynamic Optimization of Batch Reactor Based on Self-adaptive Differential Evolution Algorithm

An self-adaptive differential evolution algorithm (SADE) is introduced to solve the dynamic optimization problem of batch reactor. In SADE, each original individual has its own control parameters. Differential evolution operator is employed to search the optimization problems, and the values of weight are applied for evaluating the corresponding control parameters. Meanwhile, the weighted control parameters are used as the evolution direction of adaptively adjusting the control parameters. The experimental results show that SADE is of higher precision and fast convergence. Finally, SADE is applied for two typical dynamic optimization problems of batch reactor, and some better optimization results are obtained. Furthermore, the effect of the discrete-time degree on the optimization solution is also analyzed.