Local Search with Groups of Step Sizes

被引：1

作者：

Lopes, R. A. ^{[1
]}

Freitas, A. R. R. ^{[1
]}

Silva, R. C. P. ^{[1
]}

机构：

[1] Univ Fed Ouro Preto, Ouro Preto, MG, Brazil

来源：

OPERATIONS RESEARCH LETTERS | 2021年 / 49卷 / 03期

关键词：

Continuous optimization; Local search; Derivative-free local search; OPTIMIZATION;

D O I：

10.1016/j.orl.2021.03.009

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

Local search methods for continuous optimization problems tend to be sensitive to the choice of step sizes in their search directions. This paper presents the Local Search with Groups of Step Sizes (LSGSS) method, a derivative-free method that reactively updates groups of promising step sizes for each problem coordinate. The experiments demonstrate LSGSS could find the best solutions for each large-scale benchmark problem when compared to classical methods. (C) 2021 Elsevier B.V. All rights reserved.

引用

页码：385 / 392

页数：8

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