A Transferable Meta-Learning Phase Prediction Model for High-Entropy Alloys Based on Adaptive Migration Walrus Optimizer
Abstract
:1. Introduction
2. Related Works
2.1. Background Theory of HEAs
2.2. Walrus Optimizer (WO)
2.2.1. Initialization
2.2.2. Danger and Safety Signals
2.2.3. Migration
2.2.4. Reproduction
2.3. Nearest Neighbors-Based Adaptive DPC with an Optimized Allocation Strategy (NADPC)
3. Methodology
3.1. A Transferable Meta-Learning Algorithm Frame
3.2. Adaptive Migration Walrus Optimizer (AMWO)
3.3. Balanced-Relative Density Peaks Clustering (BRDPC)
3.3.1. Similarity Matching Algorithm
3.4. Transfer Strategy
3.4.1. Updating the History Library
4. Results and Discussion
4.1. Experimental Datasets
4.2. Comparison of Convergence for MTL-AMWO, MTL-WO, and M-FNAS
4.3. Accuracy Comparison of MTL-AMWO, MTL-WO and M-FNAS
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Index | High Entropy Alloys | ΔHmix | ΔSmix | δ | Δχ | Ω | Phase |
---|---|---|---|---|---|---|---|
0 | Co1.4CrFeMnNi | −4.115226 | 13.295461 | 1.100487 | 0.136445 | 5.792101 | SS |
1 | CrCu0.5FeMnNi | 0.098765 | 13.145213 | 1.185478 | 0.142370 | 233.301248 | SS |
2 | CoCuFeNiSn0.07 | 5.066134 | 12.050142 | 2.849306 | 0.032278 | 3.909939 | SS + IM |
3 | AlCoCrFeNiSi0.2 | −16.390533 | 14.221597 | 5.634042 | 0.120530 | 1.456954 | SS |
4 | CoFeMoNiTiVZr | −21.800000 | 16.178297 | 8.800000 | 0.254045 | 1.529411 | AM |
5 | CoCrFeMnNbNi | −12.000000 | 14.896688 | 5.900000 | 0.140643 | 2.424436 | IM |
ID | M-FNAS | MTL-WO | MTL-AMWO |
---|---|---|---|
D1 | 0.845 ± 0.023 | 0.896 ± 0.032 | 0.922 ± 0.063 |
D2 | 0.848 ± 0.037 | 0.879 ± 0.029 | 0.909 ± 0.035 |
D3 | 0.806 ± 0.030 | 0.889 ± 0.054 | 0.917 ± 0.060 |
D4 | 0.769 ± 0.046 | 0.807 ± 0.076 | 0.883 ± 0.038 |
D5 | 0.696 ± 0.036 | 0.783 ± 0.043 | 0.826 ± 0.086 |
D6 | 0.878 ± 0.041 | 0.902 ± 0.024 | 0.951 ± 0.049 |
ID | M-FNAS | MTL-WO | MTL-AMWO |
---|---|---|---|
D1 | 0.714 ± 0.021 | 0.786 ± 0.032 | 0.857 ± 0.047 |
D2 | 0.667 ± 0.033 | 0.750 ± 0.024 | 0.833 ± 0.019 |
D3 | 0.692 ± 0.041 | 0.769 ± 0.057 | 0.917 ± 0.076 |
D4 | 0.769 ± 0.035 | 0.750 ± 0.025 | 0.875 ± 0.024 |
D5 | 0.600 ± 0.025 | 0.600 ± 0.035 | 0.800 ± 0.088 |
D6 | 0.667 ± 0.027 | 0.733 ± 0.033 | 0.867 ± 0.041 |
ID | M-FNAS | MTL-WO | MTL-AMWO |
---|---|---|---|
D1 | 0.799 ± 0.024 | 0.866 ± 0.036 | 0.932 ± 0.067 |
D2 | 0.750 ± 0.028 | 0.833 ± 0.026 | 0.916 ± 0.029 |
D3 | 0.769 ± 0.037 | 0.846 ± 0.038 | 0.923 ± 0.049 |
D4 | 0.727 ± 0.055 | 0.818 ± 0.029 | 0.909 ± 0.061 |
D5 | 0.700 ± 0.051 | 0.800 ± 0.076 | 0.900 ± 0.083 |
D6 | 0.823 ± 0.071 | 0.884 ± 0.043 | 0.941 ± 0.056 |
ID | M-FNAS | MTL-WO | MTL-AMWO |
---|---|---|---|
D1 | 0.692 ± 0.031 | 0.769 ± 0.027 | 0.846 ± 0.026 |
D2 | 0.667 ± 0.045 | 0.778 ± 0.024 | 0.889 ± 0.067 |
D3 | 0.727 ± 0.062 | 0.818 ± 0.071 | 0.909 ± 0.090 |
D4 | 0.625 ± 0.074 | 0.750 ± 0.079 | 0.875 ± 0.083 |
D5 | 0.700 ± 0.085 | 0.800 ± 0.063 | 0.900 ± 0.072 |
D6 | 0.700 ± 0.049 | 0.700 ± 0.037 | 0.900 ± 0.066 |
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Hou, S.; Zhou, M.; Bai, M.; Liu, W.; Geng, H.; Yin, B.; Li, H. A Transferable Meta-Learning Phase Prediction Model for High-Entropy Alloys Based on Adaptive Migration Walrus Optimizer. Appl. Sci. 2024, 14, 9977. https://doi.org/10.3390/app14219977
Hou S, Zhou M, Bai M, Liu W, Geng H, Yin B, Li H. A Transferable Meta-Learning Phase Prediction Model for High-Entropy Alloys Based on Adaptive Migration Walrus Optimizer. Applied Sciences. 2024; 14(21):9977. https://doi.org/10.3390/app14219977
Chicago/Turabian StyleHou, Shuai, Minmin Zhou, Meijuan Bai, Weiwei Liu, Hua Geng, Bingkuan Yin, and Haotong Li. 2024. "A Transferable Meta-Learning Phase Prediction Model for High-Entropy Alloys Based on Adaptive Migration Walrus Optimizer" Applied Sciences 14, no. 21: 9977. https://doi.org/10.3390/app14219977
APA StyleHou, S., Zhou, M., Bai, M., Liu, W., Geng, H., Yin, B., & Li, H. (2024). A Transferable Meta-Learning Phase Prediction Model for High-Entropy Alloys Based on Adaptive Migration Walrus Optimizer. Applied Sciences, 14(21), 9977. https://doi.org/10.3390/app14219977