Machine learning-enabled multi-objective optimization design of process parameters for a medium-entropy alloy

Lihong Zhao; Yuyang Zhu; Cun Chen; Lihong Zhao; Yuyang Zhu; Cun Chen

doi:10.3934/bdia.2026010

Big Data and Information Analytics

2026, Volume 10, 189-214. doi: 10.3934/bdia.2026010

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Research article

Machine learning-enabled multi-objective optimization design of process parameters for a medium-entropy alloy

School of Mathematics and Statistics, Zhengzhou University, Zhengzhou 450001, China

Received: 25 July 2026 Revised: 04 August 2026 Accepted: 04 August 2026 Published: 26 August 2026

In this article, a machine learning strategy is designed to improve both the tensile strength and the elongation of a medium-entropy alloy using multi-objective optimization. In order to overcome the problem of small data sets, the linear interpolation method and Gaussian mixture model (GMM) sample generation method are integrated to augment the data. Based on the enhanced data set, the machine learning model is optimized by the sparrow search algorithm (SSA), and the multi-objective particle swarm optimization (MOPSO) algorithm is used to search the alloy process parameters with high tensile strength and elongation. Finally, 15 process parameter combinations superior to existing alloys are presented, among which three combinations, as predicted by gradient boosting regression (GBR), achieve a tensile strength exceeding 2000 MPa and an elongation of about 11%, significantly outperforming existing alloys.
- alloy design,
- machine learning,
- virtual data generation,
- performance prediction,
- multi-objective optimization,
- interpretability analysis
Citation: Lihong Zhao, Yuyang Zhu, Cun Chen. Machine learning-enabled multi-objective optimization design of process parameters for a medium-entropy alloy[J]. Big Data and Information Analytics, 2026, 10: 189-214. doi: 10.3934/bdia.2026010

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Abstract

In this article, a machine learning strategy is designed to improve both the tensile strength and the elongation of a medium-entropy alloy using multi-objective optimization. In order to overcome the problem of small data sets, the linear interpolation method and Gaussian mixture model (GMM) sample generation method are integrated to augment the data. Based on the enhanced data set, the machine learning model is optimized by the sparrow search algorithm (SSA), and the multi-objective particle swarm optimization (MOPSO) algorithm is used to search the alloy process parameters with high tensile strength and elongation. Finally, 15 process parameter combinations superior to existing alloys are presented, among which three combinations, as predicted by gradient boosting regression (GBR), achieve a tensile strength exceeding 2000 MPa and an elongation of about 11%, significantly outperforming existing alloys.

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