LAUSR

Dynamic optimization problems in chemical engineering are often described by complex differential equations, In general, Due to the complexity of the model established in practice, it is not easy to solve it with an accurate algorithm, so the study of numerical methods to solve such problems has received much attention. As a new computing model, intelligent optimization algorithm has attracted more attention in solving dynamic optimization problems because of their easy operation. Based on the analysis of the Harris Hawk Optimization algorithm, this paper proposes the Chaos Elite Harris Hawk Optimization algorithm (CEHHO), which is used to improve the performance of CEHHO using control vector parameterization to solve dynamic optimization problems of the chemical industry. First of all, when the population is initialized, the population is initialized by Opposition-based learning Logistic chaos, which improves the diversity of the population and the quality of the solution. Second, the linear decreasing escape energy factor is changed to a nonlinear decreasing escape energy factor to balance the exploration and exploitation capabilities of the algorithm. Finally, through the mutation strategy guided by elite individuals, the algorithm can jump out of the local optimum. We use 8 test functions and 5 classical chemical optimization problems to evaluate the feasibility of the algorithm and compare and analyze the research results with other solving methods, showing the superiority of the CEHHO algorithm.