Evolutionary optimization with adaptive surrogates and its application in crude oil distillation

Surrogate modelling and model management are key points for evolutionary optimization of chemical processes. This paper proposes an evolutionary algorithm with the help of adaptive surrogate functions (EASF), in which approximate models' establishment and management are combined to search the optimal result. To construct an appropriate surrogate model, a new hybrid modelling framework with adaptive Radial Basis Functions (RBF) (ARBF) is put forward. Different from most neural network modelling methods, ARBF is able to adaptively adjust the sample size by current approximation errors to effectively take into account the tradeoff between approximation accuracy and sample size. For model management, an approximation error fuzzy control strategy (AEFCS) is introduced. AEFCS in combination with ARBF can effectively perform exploratory and exploitative search in the evolutionary optimization. The superiority of EASF is demonstrated by the simulation results on three benchmark problems. To illustrate the performance of EASF further, it is employed to optimize the operating conditions of crude oil distillation process, and satisfactory results are obtained.