Robust optimization using hybrid differential evolution and sequential quadratic programming

Abstract

Although robust optimization can find solutions to engineering problems with uncertainty, the computational inefficiency of robust optimization methods is still a major concern. In this article, a new hybrid robust optimization method, namely differential evolution–sequential quadratic programming–robust optimization (DE-SQP-RO), is presented to find global robust optima for nonlinear robust optimization problems. The proposed algorithm is conducted under the structure of DE-RO, with SQP-RO acting as a local optimizer. Two criteria and switch indices are developed to indicate when the algorithm should switch from DE-RO to SQP-RO and vice versa. One numerical and two engineering examples are tested to demonstrate the applicability of the proposed algorithm. The results show that the hybrid algorithm uses 29–45% of the number of function evaluations required by DE-RO, and the robust solutions obtained by the hybrid algorithm are even better for certain examples.