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.