Improved NSGA-III using transfer learning and centroid distance for dynamic multi-objective optimization
Abstract Multi-objective problems in real world are often contradictory and even change over time. As we know, how to find the changing Pareto front quickly and accurately is challenging during the process of solving dynamic multi-objective optimization problems (DMOPs). In addition, most solutions...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2021-11-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-021-00570-z |