Parallel Global Search Algorithm with Local Tuning for Solving Mixed-Integer Global Optimization Problems

被引：0

作者：

Barkalov, K. A. ^{[1
]}

Gergel, V. P. ^{[1
]}

Lebedev, I. G. ^{[1
]}

机构：

[1] Lobachevskii State Univ Nizhny Novgorod, Nizhnii Novgorod 603950, Russia

来源：

LOBACHEVSKII JOURNAL OF MATHEMATICS | 2021年 / 42卷 / 07期

关键词：

global optimization; non-convex constraints; mixed-integer problems; local tuning; parallel algorithms;

D O I：

10.1134/S1995080221070040

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In this paper, we consider mixed-integer global optimization problems and propose a parallel algorithm for solving problems of this class based on information-statistical approach for solving continuous global optimization problems. Within this algorithm, we suggest using a local tuning scheme based on the assumption that the multiextremality of the discussed problem is weak. We also compare the sequential version of the algorithm with other similar methods. The effectiveness of parallelizing the algorithm has been confirmed by solving a series of mixed-integer global optimization problems on the Lobachevskii supercomputer.

引用

页码：1492 / 1503

页数：12

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