.png){kind=icon}
返回
A Classifier-Assisted Level-Based Learning Swarm Optimizer for Expensive Optimization
.png){kind=icon}
PRE
AI
DOI：10.1109/TEVC.2020.3017865.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Surrogate-assisted evolutionary algorithms (SAEAs) have become one popular method to solve complex and computationally expensive optimization problems. However, most existing SAEAs suffer from performance degradation with the dimensionality increasing. To solve this issue, this article proposes a classifier-assisted level-based learning swarm optimizer on the basis of the level-based learning swarm optimizer (LLSO) and the gradient boosting classifier (GBC) to improve the robustness and scalability of SAEAs. Particularly, the level-based learning strategy in LLSO has a tight correspondence with the classification characteristic by setting the number of levels in LLSO to be the same as the number of classes in GBC. Together, the classification results feedback the distribution of promising candidates to accelerate the evolution of the optimizer, while the evolved population helps to improve the accuracy of the classifier. To select informative and valuable candidates for real evaluations, we devise an L1-exploitation strategy to extensively exploit promising areas. Then, the candidate selection is conducted between the predicted L1 offspring and the already real-evaluated L1 individuals based on their Euclidean distances. Extensive experiments on commonly used benchmark functions demonstrate that the proposed optimizer can achieve competitive or better performance with a very small training dataset compared with three state-of-the-art SAEAs.
Keyword:
Expensive optimization
gradient boosting classifier (GBC)
large-scale optimization
level-based learning swarm optimizer (LLSO)
surrogate-assisted evolutionary algorithm (SAEA)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
12
论文数:
1.9K
被引数:
2.4W
机构
引用论文
A Comprehensive Analysis of Cyber Data Breaches and Their Resulting Effects on Shareholder Wealth
PREAI
分享收藏分享收藏Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimization of a Trauma System创伤系统的数据驱动代理辅助多目标进化优化
OAAI
OAAI分享收藏Highly efficient electrocatalytic hydrogen production by nickel promoted molybdenum sulfide microspheres catalysts镍促进硫化钼微球催化剂的高效电催化制氢.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏分享收藏
查看更多内容