A Generator of Multiextremal Test Classes With Known Solutions for Black-Box-Constrained Global Optimization

被引:6
|
作者
Sergeyev, Yaroslav D. D. [1 ,2 ,3 ]
Kvasov, Dmitri E. E. [1 ,2 ]
Mukhametzhanov, Marat S. S. [1 ,2 ]
机构
[1] Univ Calabria, DIMES, Arcavacata Di Rende 87036, Italy
[2] IITMM, Lobachevsky State Univ, Nizhnii Novgorod 603950, Russia
[3] Italian Natl Res Council, Inst High Performance Computingand Networking, I-87036 Arcavacata Di Rende, Italy
基金
俄罗斯科学基金会;
关键词
Optimization; Generators; Linear programming; Benchmark testing; Visualization; Software algorithms; Software; Benchmarking optimization software; black-box global optimization; GKLS-generator; known global minima; problems with nonlinear constraints; ALGORITHMS; SOFTWARE;
D O I
10.1109/TEVC.2021.3139263
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A generator of classes of multidimensional test problems for benchmarking continuous constrained global optimization methods is proposed. It is based on the generator of test classes for global optimization proposed in 2003 by Gaviano, Kvasov, Lera, and Sergeyev and extends the previous generation procedure from the box-constrained case to the case of nonlinear constraints. The user has the possibility to fix the difficulty of tests in an intuitive way by choosing several types of constraints. A detailed information (including the global solution) for each of 100 problems in each generated class is provided to the user. The generator is particularly suited for testing black-box optimization algorithms that normally address low or medium dimensional problems with hard to evaluate objective functions.
引用
收藏
页码:1261 / 1270
页数:10
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