An Evolutionary Algorithm for Solving Large-Scale Robust Multiobjective Optimization Problems
针对高维决策空间中稀疏最优解的鲁棒多目标优化问题，提出一种进化算法，通过存档分离最优性与鲁棒性、自适应引导向量生成鲁棒解，以及无额外扰动的鲁棒性指标评估，在测试集和实际应用中优于现有算法。
Robust multiobjective 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 article 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 the large-scale RMOPs. Our experimental results demonstrate that the proposed algorithm has significant advantages over the state-of-the-art evolutionary algorithms in terms of optimality and robustness, on both the proposed test suite and practical applications.