The intelligent optimization algorithms have been widely used in solving dynamic optimization problems for their ability to converge to global optimum at a specific probability without being trapped in local optimums. However

the practical industrial application fields of such random mechanism-based algorithms are restricted due to their demerits

such as slow convergence and low searching efficiency.Therefore

an improved knowledge-based evolutionary algorithm structure was presented for improving the efficiency of intelligent optimization algorithms which were used to solve dynamic optimization problems. The structure included a discretization method of candidate solutions in time and control domains

the evolution of the repository

population evolution steered by knowledge. When applied to dynamic optimization problems of four typical chemical processes

such as batch reactor

which owned distinguishing control feature

this algorithm demonstrated its competitive optimal searching ability

meanwhile verifying its satisfying convergence efficiency by using a smaller scale of population guided by knowledge and consuming less computational cost.