Surrogate-assisted optimization has attracted much attention due to its superiority in solving expensive optimization problems. However, relatively little work has been dedicated to addressing expensive constrained multi-objective discrete optimization problems although there are many such problems in the real world. Hence, a surrogate-assisted evolutionary algorithm is proposed in this paper for this kind of problem. Specifically, random forest models are embedded in the framework of the evolutionary algorithm as surrogates to improve approximate accuracy for discrete optimization problems. To enhance the optimization efficiency, an improved stochastic ranking strategy based on the fitness mechanism and adaptive probability operator is presented, which also takes into account both convergence and diversity to advance the quality of candidate solutions. To validate the proposed algorithm, it is comprehensively compared with several well-known optimization algorithms on several benchmark problems. Numerical experiments are demonstrated that the proposed algorithm is very promising for the expensive constrained multi-objective discrete optimization problems.

代理辅助优化因其在解决昂贵的优化问题上的优势而备受关注。但是，尽管在现实世界中存在许多此类问题，但是致力于解决昂贵的受限多目标离散优化问题的工作却相对较少。因此，针对此类问题，本文提出了一种代理辅助进化算法。具体来说，随机森林模型作为替代算法嵌入进化算法的框架中，以提高离散优化问题的近似精度。为了提高优化效率，提出了一种基于适应度机制和自适应概率算子的改进随机排序策略，这也考虑了融合和多样性，以提高候选解决方案的质量。为了验证所提出的算法，在几个基准问题上与几种著名的优化算法进行了全面比较。数值实验表明，该算法对于昂贵的约束多目标离散优化问题具有很好的应用前景。