THE MESH ADAPTIVE DIRECT SEARCH ALGORITHM FOR GRANULAR AND DISCRETE VARIABLES

被引：43

作者：

Audet, Charles ^{[1
,2
]}

Le Digabel, Sebastien ^{[1
,2
]}

Tribes, Christophe ^{[1
,2
]}

机构：

[1] Polytech Montreal, GERAD, CP 6079,Succ Ctr Ville, Montreal, PQ H3C 3A7, Canada

[2] Polytech Montreal, Dept Math & Genie Ind, CP 6079,Succ Ctr Ville, Montreal, PQ H3C 3A7, Canada

来源：

SIAM JOURNAL ON OPTIMIZATION | 2019年 / 29卷 / 02期

基金：

加拿大自然科学与工程研究理事会;

关键词：

blackbox optimization; derivative-free optimization; mesh adaptive direct search; granular variables; discrete variables; SURROGATE MODEL ALGORITHM; NELDER-MEAD ALGORITHM; GENETIC-ALGORITHM; OPTIMIZATION PROBLEMS; CONVERGENCE; DECOMPOSITION; DESIGN; NUMBER;

D O I：

10.1137/18M1175872

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The mesh adaptive direct search (Mads) algorithm is designed for blackbox optimization problems for which the functions defining the objective and the constraints are typically the outputs of a simulation seen as a blackbox. It is a derivative-free optimization method designed for continuous variables and is supported by a convergence analysis based on the Clarke calculus. This work introduces a modification to the Mads algorithm so that it handles granular variables, i.e., variables with a controlled number of decimals. This modification involves a new way of updating the underlying mesh so that the precision is progressively increased. A corollary of this new approach is the ability to treat discrete variables. Computational results are presented using the NOMAD software, the free C++ distribution of the Mads algorithm.

引用

页码：1164 / 1189

页数：26

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[51] Solving spread spectrum radar polyphase code design problem by tabu search and variable neighbourhood search [J].