An Evolutionary Algorithm for Solving Large-Scale Robust Multi-Objective Optimization Problems

Robust multi-objective optimization problems (RMOPs) widely exist in real-world applications, which introduce a variety of uncertainty in optimization models. While some evolutionary algorithms have been developed to find optimal solutions robust to uncertainty, they are ineffective to handle RMOPs in high-dimensional decision spaces. Focusing on the large-scale RMOPs with sparse optimal solutions, this paper proposes an evolutionary algorithm with novel strategies for the selection, generation, and evaluation of robust solutions. In order to handle the uncertainty in the optimization models, we first introduce an archive to separately consider optimality and robustness, which can achieve the selection of robust solutions effectively at a low cost. Based on the robust knowledge extracted from the archive, a guiding vector is adaptively updated to facilitate the generation of robust solutions in high-dimensional decision spaces. With the assistance of the guiding vector, a robustness indicator is suggested to assist in the evaluation of robust solutions without additional perturbations. Besides, we design a test suite to evaluate the performance of the proposed algorithm on large-scale RMOPs. Our experimental results demonstrate that the proposed algorithm has significant advantages over state-of-the-art evolutionary algorithms in terms of optimality and robustness, on both the proposed test suite and practical applications.