Despite that the reference point based preference articulation plays a vital role in evolutionary multi- and many-objective optimization, three issues remain challenging. First, the performance of reference point based preference articulation largely depends on the location of the reference point. Second, the parameter settings for controlling the region of interest are not robust to the Pareto optimal fronts with different complicated shapes. Third, most existing methods have poor scalability to the number of objectives. To meet these challenges, we propose to reformulate preferences into constraints for evolutionary multi- and many-objective optimization. Extensive experiments on a variety of benchmark problems are conducted to demonstrate the effectiveness of our proposed method.

尽管基于参考点的偏好表达在进化多目标和多目标优化中起着至关重要的作用，但三个问题仍然具有挑战性。首先，基于参考点的偏好表达的性能很大程度上取决于参考点的位置。其次，用于控制感兴趣区域的参数设置对于具有不同复杂形状的帕累托最优前沿不稳健。第三，大多数现有方法对目标数量的可伸缩性较差。为了应对这些挑战，我们建议将偏好重新设定为约束，以进行进化的多目标和多目标优化。进行了各种基准问题的广泛实验，以证明我们提出的方法的有效性。