An Adaptive Coevolution Method for Efficient Robust Optimization Under Interval Uncertainty

ABSTRACT Interval‐based multi‐objective robust optimization aims to achieve high‐performance solutions insensitive to uncertainty, has garnered significant attention. However, efficiently analyzing the maximum fluctuations of solutions in both objective and constraint functions to assess their robustness remains challenging. To address this issue, this article proposes an adaptive coevolution method, which can evaluate the maximum fluctuations of multiple candidate solutions with respect to a function in a single run. This method is integrated with the multi‐objective evolutionary algorithm (MOEA) to develop a framework termed adaptive coevolution‐based multi‐objective robust optimization (AC‐MORO) for solving multi‐objective robust optimization problems. To evaluate the performance of AC‐MORO, it is compared with MODE‐RO on a set of benchmark problems; meanwhile, a performance metric is proposed to test the accuracy of the adaptive coevolution method in analyzing the robustness of solutions. The impact of various parameter settings on the efficiency of the proposed method is also investigated. Subsequently, a variant of the adaptive coevolution method is explored to further enhance the performance of AC‐MORO. Finally, AC‐MORO is applied to address robust optimization problems in practical engineering.