Interactive data-driven multiobjective optimization of metallurgical properties of microalloyed steels using the DESDEO framework

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F23%3A00374006" target="_blank" >RIV/68407700:21220/23:00374006 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.engappai.2023.105918" target="_blank" >https://doi.org/10.1016/j.engappai.2023.105918</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.engappai.2023.105918" target="_blank" >10.1016/j.engappai.2023.105918</a>

Result language

angličtina

Original language name

Interactive data-driven multiobjective optimization of metallurgical properties of microalloyed steels using the DESDEO framework

Original language description

Solving real-life data-driven multiobjective optimization problems involves many complicated challenges. These challenges include preprocessing the data, modelling the objective functions, getting a meaningful formulation of the problem, and supporting decision makers to find preferred solutions in the existence of conflicting objective functions. In this paper, we tackle the problem of optimizing the composition of microalloyed steels to get good mechanical properties such as yield strength, percentage elongation, and Charpy energy. We formulate a problem with six objective functions based on data available and support two decision makers in finding a solution that satisfies them both. To enable two decision makers to make meaningful decisions for a problem with many objectives, we create the so-called MultiDM/IOPIS algorithm, which combines multiobjective evolutionary algorithms and scalarization functions from interactive multiobjective optimization methods in novel ways. We use the software framework called DESDEO, an open-source Python framework for interactively solving multiobjective optimization problems, to create the MultiDM/IOPIS algorithm. We provide a detailed account of all the challenges faced while formulating and solving the problem. We discuss and use many strategies to overcome those challenges. Overall, we propose a methodology to solve real-life data-driven problems with multiple objective functions and decision makers. With this methodology, we successfully obtained microalloyed steel compositions with mechanical properties that satisfied both decision makers.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20501 - Materials engineering

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Engineering Applications of Artificial Intelligence

ISSN

0952-1976

e-ISSN

1873-6769

Volume of the periodical

120

Issue of the periodical within the volume

105918

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

15

Pages from-to

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UT code for WoS article

000927780300001

EID of the result in the Scopus database

2-s2.0-85147195173