期刊
KNOWLEDGE-BASED SYSTEMS
卷 236, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2021.107747
关键词
Heterogeneous ensemble surrogate; Infill criterion; Computationally expensive problems; Radial basis function; Surrogate-assisted evolutionary algorithms
资金
- National Outstanding Youth Science Foundation of China [61922072]
- National Natural Science Foundation of China [61803054, 61876169]
- Fundamental Research Funds for the Central Universities [2019CDJSK04XK23]
Surrogate-assisted evolutionary algorithms have gained significant attention in solving expensive optimization problems. In this study, a twofold infill criterion-driven heterogeneous ensemble surrogate-assisted neighborhood field optimization algorithm (HESNFO) is proposed, which takes into account the diversity and accuracy of surrogates to speed up the optimization process.
Over the past decade, surrogate-assisted evolutionary algorithms (SAEAs) have received considerable attention in solving expensive optimization problems from both academia and industry. In SAEAs, model management plays a key role in the all-encompassing use of surrogates and data. Constructing sophisticated management strategies and high-fidelity surrogates, nevertheless, remains an urgent and foundational task. In this context, we propose a twofold infill criterion-driven heterogeneous ensemble surrogate-assisted neighborhood field optimization algorithm (HESNFO). The proposed algorithm takes into account both the diversity and accuracy of surrogates to speed up the optimization process. A twofold infill criterion is presented to strike a balance between exploration and exploitation on the basis of updating the surrogates online. Meanwhile, to enhance the diversity of surrogates, a heterogeneous ensemble surrogate consisting of multiple radial basis function models with different architectures and different inputs has been built. Finally, our experimental results on several benchmark problems as well as an electromagnetic acoustic transducers optimization instance demonstrate that the proposed algorithm is superior. (C) 2021 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据