Engineering optimization problems usually involve computationally expensive simulations and many design variables. Solving such problems in an efficient manner is still a major challenge. In this paper, a generalized surrogate-assisted evolutionary algorithm is proposed to solve such high-dimensional expensive problems. The proposed algorithm is based on the optimization framework of the genetic algorithm (GA). This algorithm proposes to use a surrogate-based trust region local search method, a surrogate-guided GA (SGA) updating mechanism with a neighbor region partition strategy and a prescreening strategy based on the expected improvement infilling criterion of a simplified Kriging in the optimization process. The SGA updating mechanism is a special characteristic of the proposed algorithm. This mechanism makes a fusion between surrogates and the evolutionary algorithm. The neighbor region partition strategy effectively retains the diversity of the population. Moreover, multiple surrogates used in the SGA updating mechanism make the proposed algorithm optimize robustly. The proposed algorithm is validated by testing several high-dimensional numerical benchmark problems with dimensions varying from 30 to 100, and an overall comparison is made between the proposed algorithm and other optimization algorithms. The results show that the proposed algorithm is very efficient and promising for optimizing high-dimensional expensive problems.

中文翻译：

---

高维昂贵问题的高效广义代理辅助进化算法

工程优化问题通常涉及计算成本高的模拟和许多设计变量。以有效的方式解决此类问题仍然是一项重大挑战。在本文中，提出了一种广义代理辅助进化算法来解决此类高维昂贵问题。所提出的算法基于遗传算法（GA）的优化框架。该算法提出在优化过程中使用基于代理的信任域局部搜索方法、代理引导的遗传算法（SGA）更新机制与邻域划分策略和基于简化克里金的期望改进填充准则的预筛选策略。 . SGA 更新机制是该算法的一个特点。这种机制在代理和进化算法之间进行了融合。邻域划分策略有效地保留了种群的多样性。此外，SGA 更新机制中使用的多个代理使所提出的算法优化鲁棒。通过对几个维度从30到100不等的高维数值基准问题进行测试，对该算法进行了验证，并与其他优化算法进行了总体比较。结果表明，该算法在优化高维昂贵问题方面非常有效且有前景。SGA 更新机制中使用的多个代理使所提出的算法进行了稳健的优化。通过对几个维度从30到100不等的高维数值基准问题进行测试，对该算法进行了验证，并与其他优化算法进行了总体比较。结果表明，该算法在优化高维昂贵问题方面非常有效且有前景。SGA 更新机制中使用的多个代理使所提出的算法进行了稳健的优化。通过对几个维度从30到100不等的高维数值基准问题进行测试，对该算法进行了验证，并与其他优化算法进行了总体比较。结果表明，该算法在优化高维昂贵问题方面非常有效且有前景。