Evolutionary algorithms face significant challenges when it comes to solving expensive multi-objective optimization problems, which require costly evaluations. One of the most popular approaches to addressing this issue is to use surrogate models, which can replace the expensive real function evaluations with cheaper approximations. However, in many surrogate-assisted evolutionary algorithms (SAEAs), the process of offspring generation has not received sufficient attention. In this paper, we propose a novel framework for expensive multi-objective optimization called RM-SAEA, which utilizes a regularity model (RM) operator to generate offspring more effectively. The regularity model operator is combined with a general genetic algorithm operator to create a heterogeneous offspring generation module that can better approximate the Pareto front. Moreover, to overcome the data deficiency issue in expensive optimization scenarios, we employ a data augmentation strategy while training the regularity model. Finally, we embed three representative SAEAs into the proposed RM-SAEA to demonstrate its efficacy. Experimental results on several benchmark test suites with up to 10 objectives and real-world applications show that RM-SAEA achieves superior overall performance compared to eight state-of-the-art algorithms. By focusing on more effective offspring generation and addressing data deficiencies, our proposed framework is able to generate better approximations of the Pareto front and improve the optimization process in expensive multi-objective optimization scenarios.

中文翻译：

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替代辅助进化算法中基于规则模型的后代生成，用于昂贵的多目标优化

在解决昂贵的多目标优化问题时，进化算法面临着重大挑战，这需要昂贵的评估。解决这个问题最流行的方法之一是使用代理模型，它可以用更便宜的近似值代替昂贵的实函数评估。然而，在许多代理辅助进化算法（SAEA）中，后代的生成过程并未受到足够的重视。在本文中，我们提出了一种名为 RM-SAEA 的昂贵多目标优化的新颖框架，它利用正则模型（RM）算子更有效地生成后代。正则模型算子与通用遗传算法算子相结合，创建了能够更好地逼近帕累托前沿的异构后代生成模块。此外，为了克服昂贵的优化场景中的数据缺乏问题，我们在训练规律性模型时采用了数据增强策略。最后，我们将三个代表性 SAEA 嵌入到提议的 RM-SAEA 中以证明其功效。具有多达 10 个目标的多个基准测试套件和实际应用的实验结果表明，与八种最先进的算法相比，RM-SAEA 实现了卓越的整体性能。通过专注于更有效的后代生成和解决数据缺陷，我们提出的框架能够生成更好的帕累托前沿近似值，并改进昂贵的多目标优化场景中的优化过程。