A Simple Yet Effective Approach to Robust Optimization Over Time

被引：0

作者：

Adam, Lukas ^{[1
]}

Yao, Xin ^{[1
]}

机构：

[1] Southern Univ Sci & Technol, Shenzhen Key Lab Computat Intelligence, Univ Key Lab Evolving Intelligent Syst Guangdong, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China

来源：

2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019) | 2019年

基金：

中国国家自然科学基金;

关键词：

dynamic optimization; robust optimization; robust optimization over time; uniform sampling; particle swarm optimization;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Robust optimization over time (ROOT) refers to an optimization problem where its performance is evaluated over a period of future time. Most of the existing algorithms use particle swarm optimization combined with another method which predicts future solutions to the optimization problem. We argue that this approach may perform subpar and suggest instead a method based on a random sampling of the search space. We prove its theoretical guarantees and show that it significantly outperforms the state-of-the-art methods for ROOT.

引用

页码：680 / 688

页数：9

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