Interactive data-driven multiobjective optimization of metallurgical properties of microalloyed steels using the DESDEO framework
Saini, B. S., Chakrabarti, D., Chakraborti, N., Shavazipour, B., & Miettinen, K. (2023). Interactive data-driven multiobjective optimization of metallurgical properties of microalloyed steels using the DESDEO framework. Engineering Applications of Artificial Intelligence, 120, Article 105918. https://doi.org/10.1016/j.engappai.2023.105918
Published inEngineering Applications of Artificial Intelligence
DisciplineMultiobjective Optimization GroupLaskennallinen tiedeMultiobjective Optimization GroupComputational Science
© 2023 the Authors
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. ...
ISSN Search the Publication Forum0952-1976
data-driven evolutionary computation multiple criteria optimization surrogate-assisted optimization multiple decision makers interactive optimization open-source software monitavoiteoptimointi optimointi metallurgia päätöksentukijärjestelmät interaktiivisuus metalliseokset avoin lähdekoodi fysikaaliset ominaisuudet
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Related funder(s)Academy of Finland
Funding program(s)Academy Project, AoF
Additional information about fundingThis research was partly funded by the Academy of Finland (grant 322221). The research is related to the thematic research area Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO), jyu.fi/demo, at the University of Jyvaskyla.
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