An adaptive surrogate assisted differential evolutionary algorithm for high dimensional constrained problems.

Differential evolution (DE) is a competitive algorithm for constrained optimization problems (COPs). In this study, in order to improve the efficiency and accuracy of the DE for high dimensional problems, an adaptive surrogate assisted DE algorithm, called ASA-DE is suggested. In the ASA, several kinds of surrogate modeling techniques are integrated. Furthermore, to avoid violate the constraints and obtain better solution simultaneously, adaptive strategies for population size and mutation are also suggested in this study. The suggested adaptive population strategy which controls the exploring and exploiting states according to whether algorithm find enough feasible solution is similar to a state switch. The mutation strategy is used to enhance the effect of state switch based on adaptive population size. Finally, the suggested ASA-DE is evaluated on the benchmark problems from congress on evolutionary computation (CEC) 2017 constrained real parameter optimization. The experimental results show the proposed algorithm is a competitive one compared to other state-of-the-art algorithms. • An adaptive surrogate assisted DE algorithm is suggested. • Several kinds of surrogate modeling techniques are integrated in suggested algorithm. • An adaptive population size strategy based on pfeas is suggested. • Parameter adaptation strategy based on success-history is suggested. • The suggested algorithm is evaluated by the benchmark problems from CEC2017. [ABSTRACT FROM AUTHOR]