发表机构
AVIC Shenyang Engine Design Institute（中国航空工业集团公司沈阳发动机设计研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高维昂贵黑箱优化问题，提出知识迁移辅助的高效全局优化算法KT-EGO，通过划分低维子空间、代理数据融合和自适应搜索，在12个基准函数及28维压气机叶片设计中验证了有效性。
AI 中文摘要
许多工程问题涉及优化高维昂贵黑箱（HEB）设计空间。为了高效求解此类问题，我们提出了一种知识迁移辅助的高效全局优化（EGO）算法，命名为KT-EGO，该算法扩展了EGO算法以求解更高维度（即$d>20$）的问题。具体而言，原始设计空间被划分为多个低维子集设计空间。更重要的是，为了从子集设计空间中提取信息以加速完整优化的进程，我们在KT-EGO中提出了一种基于代理模型的数据融合策略。此外，设计了一种具有自适应变量范围的搜索策略，以增强对有前景区域的开发。为了展示所提算法的有效性，将其与最先进算法在12个基准函数和一个28维的压气机叶片工程设计优化问题上进行了比较，这充分验证了KT-EGO在求解HEB问题上的有效性。
英文摘要
Many engineering problems involve optimizing a high-dimensional expensive black-box (HEB) design space. To solve such problems efficiently, we propose a knowledge transfer assisted efficient global optimization (EGO) algorithm, labeled as KT-EGO, which extends the EGO algorithm for solving problems over higher dimensions (i.e., $d>20$). Specifically, the original design space is divided into several low-dimensional subset design spaces. More importantly, in order to extract information from the subset design spaces to accelerate the progress of full optimization, we propose a surrogate-based data fusion strategy in KT-EGO. And further, a searching strategy with an adaptive variable range is devised to enhance the exploitation of promising areas. To show the effectiveness of our proposed algorithm, it is compared against the state-of-the-art algorithms over 12 benchmark functions and a 28-dimensional engineering optimization for the design of compressor blade, which fully validates the effectiveness of the KT-EGO for solving HEB problems.
Comments25 pages, 12 figures; abridged author manuscript
Journal refEngineering Optimization 55(12), 2015-2033 (2023)
DOI：10.1080/0305215X.2022.2139374
