Machine Learning Techniques for Solving Constrained Engineering Problems

被引：0

作者：

Garbaya, Amel ^{[1
]}

Kallel, Imen ^{[1
]}

Fakhfakh, Mourad ^{[1
]}

Siarry, Patrick ^{[2
]}

机构：

[1] Univ Sfax, ESSE Lab, ENETcom, Sfax, Tunisia

[2] Univ Paris Est Creteil, Creteil, France

来源：

2022 2ND INTERNATIONAL CONFERENCE ON INNOVATIVE RESEARCH IN APPLIED SCIENCE, ENGINEERING AND TECHNOLOGY (IRASET'2022) | 2022年

关键词：

Machine learning; Benchmark Functions; Engineering design problems; MSE; RMSE; MAE;

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：

This paper focuses on the application of the supervised machine learning technique. The main objective is to construct models of objective functions. Sixteen different varieties of benchmark test functions and three well-known engineering design problems are evaluated by machine learning technique. The Artificial Neural Networks (ANNs) technique is used for constructing models. For the sake of accuracy check, three metrics are used; Mean Square Error (MSE), Root Mean Square Error (RMSE) and Maximum Absolute Error (MAE).

引用

页码：967 / 970

页数：4

共 13 条

[1] Review: Machine learning techniques in analog/RF integrated circuit design, synthesis, layout, and test [J].

Afacan, Engin ;

Lourenco, Nuno ;

Martins, Ricardo ;

Dundar, Gunhan .

INTEGRATION-THE VLSI JOURNAL, 2021, 77 :113-130

[2]

[Anonymous], 2008, P 25 INT C MACH LEAR, DOI DOI 10.1145/1390156.1390177

[3]

Demuth H., 1992, NEURAL NETWORK TOOLB

[4]

Forrester I. J., 2008, DBLP, DOI DOI 10.1002/9780470770801

[5]

Ghayoumi M., 2021, Deep Learning in Practice

[6]

Hastie T., 2009, The Elements of Statistical Learning: Data Mining, Inference and Prediction, V2nd, DOI [10.1007/978-0-387-84858-7, DOI 10.1007/978-0-387-84858-7]

[7] A parallel boundary search particle swarm optimization algorithm for constrained optimization problems [J].

Liu, Zhao ;

Li, Zeyang ;

Zhu, Ping ;

Chen, Wei .

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2018, 58 (04) :1505-1522

[8]

Murphy KP, 2012, MACHINE LEARNING: A PROBABILISTIC PERSPECTIVE, P1

[9]

Rocha F., 2013, IEEE C EVOLUTIONARY

[10] THE PERCEPTRON - A PROBABILISTIC MODEL FOR INFORMATION-STORAGE AND ORGANIZATION IN THE BRAIN [J].

ROSENBLATT, F .

PSYCHOLOGICAL REVIEW, 1958, 65 (06) :386-408

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